Do commercial fisheries display optimal foraging? The case of longline fishers in competition with odontocetes
Bibliographic record
Abstract
Depredation in longline fisheries by odontocete whales is a worldwide growing issue, having substantial socioeconomic consequences for fishers as well as conservation implications for both fish resources and the depredating odontocete populations. An example of this is the demersal longline fishery operating around the Crozet Archipelago and Kerguelen Island, southern Indian Ocean, where killer whales (Orcinus orca) and sperm whales (Physeter macrocephalus) depredate hooked Patagonian toothfish (Dissostichus eleginoides). It is of great interest to better understand relationships of this modern fishery with its environment. Thus, we examined the factors influencing the decision-making process of fishers facing such competition while operating on a patch. Using optimal foraging theory as the underlying hypothesis, we determined that the probability captains left an area decreases with increasing fishing success, whereas in presence of competition from odontocete whales, it increases. Our study provides strong support that fishers behave as optimal foragers in this specific fishery. Considering that captains are optimal foragers and thus aim at maximizing the exploitation of the resources, we highlight possible risks for the long-term sustainability of the local ecosystems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".