A novel model of predator–prey interactions reveals the sensitivity of forage fish: piscivore fishery trade-offs to ecological conditions
Bibliographic record
Abstract
Abstract Ecosystem-based fisheries management seeks to consider trade-offs among management objectives for interacting species, such as those that arise through predator–prey linkages. In particular, fisheries-targeting forage fish (small and abundant pelagic fish) might have a detrimental effect on fisheries-targeting predators that consume them. However, complexities in ecological interactions might dampen, negate, or even reverse this trade-off, because small pelagic fish can be important predators on egg stages of piscivorous fish. Further, the strength of this trade-off might depend on the extent to which piscivorous fish targeted by fisheries regulate forage species productivity. Here, we developed a novel delay-differential bioeconomic model of predator–prey and fishing dynamics to quantify how much egg predation or weak top-town control affects the strength of trade-off between forage and piscivore fisheries, and to measure how ecological interactions dictate policies that maximize steady-state profits. We parameterized the model based on ecological and economic data from the North Sea Atlantic cod (Gadus morhua) and Atlantic herring (Clupea harengus). The optimal policy was very sensitive to the ecological interactions (either egg predation or weak top-down control of forage by predators) at relatively low forage prices but was less sensitive at high forage fish prices. However, the optimal equilibrium harvest rates on forage and piscivores were not substantially different from what might be derived through analyses that did not consider species interactions. Applying the optimal multispecies policy would produce substantial losses (>25%) in profits in the piscivore fishery, and the extent of loss was sensitive to ecological scenarios. While our equilibrium analysis is informative, a dynamic analysis under similar ecological scenarios is necessary to reveal the full economic and ecological benefits of applying ecosystem-based fishery management policies to predator–prey fishery systems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".