No free lunch: estimating the biomass and ex-vessel value of target catch lost to depredation by odontocetes in the Hawai‘i longline tuna fishery
Bibliographic record
Abstract
Depredation by marine predators causes economic losses and impacts depredating species and fish stocks. To understand these impacts, it is important to accurately estimate catch losses from depredation. Pelagic longline fisheries are susceptible to depredation, and depredation is difficult to quantify, because gear is suspended in the water column away from the vessel for extended periods. In the present study, we used fisheries data and a novel modeling approach to estimate catch removal by odontocetes in the Hawai‘i deep-set longline fishery. We estimated annual biomass and economic value lost to depredation of three of the most commonly landed species as approximately 100 t and 1 million USD, respectively, during 2012–2018. The median cost on sets when depredation occurred was $600 USD, with the worst 10% of sets experiencing losses exceeding $2300 USD. We also identified broad-scale spatiotemporal patterns and hotspots of depredation across the range of the fishery. Our findings quantify the ecological and economic implications of this interaction, and our methods can be applied in similar fisheries elsewhere to assess the impacts of depredation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".