Applying a joint-species spatio-temporal model to guide the reduction of bycatch in longline fisheries with a case study on swordfish in the Southwestern Pacific Ocean
Bibliographic record
Abstract
Abstract Longline fisheries targeting tunas often result in substantial bycatch of other pelagic migratory species, leading to ecological and economic challenges for fisheries management. This study employed Vector Autoregressive Spatio-Temporal (VAST) models to identify effective spatial fishery closures to support swordfish (SWO) bycatch management while maintaining viable bigeye tuna (BET) fisheries in the Southwestern Pacific Ocean. We compared single- and joint-species modelling approaches within the VAST framework to evaluate whether accounting for species associations could improve our understanding of spatial distribution patterns for SWO and BET using catch and effort data from Taiwan’s distant-water longline (TWN DWLL) fishery during 2016–2023 in the high seas north of 20°S (0°–20°S, 110°–165°W). To evaluate potential spatial closures, we developed a bycatch risk index (BRI) that integrates both SWO bycatch hotspots and BET density, allowing for systematic analysis of trade-offs between bycatch reduction and fishery operations. We also assessed the potential impacts of implementing spatial closures on SWO and BET catches for major fleets (i.e. Japan, China, and Korea) operating in the study area. Results indicated that the joint-species model demonstrated superior performance, with a lower AIC value and a 1.1–3.0% higher predictive probability in spatially cross-validation compared to single-species models. Our analysis found a strong negative spatial correlation between SWO and BET, with a persistent high-risk area for SWO bycatch between 0°–5°S and 110°–140°W. Analysis of various BRI thresholds showed that moderate thresholds (60% BRI) provided an optimal balance, achieving an expected 36% reduction in SWO catch while limiting BET catch reduction to 12%, and fishing effort reduction to 18% for the TWN DWLL. In addition, the joint-species model generally outperformed single-species approaches for various BRI thresholds, demonstrating more spatially efficient closures by better accounting for species associations. This pattern was consistent across other major fleets operating in the SPO, including Japan, China, and Korea. The methodology developed here offers a quantitative framework for evaluating conservation-fishery viability trade-offs in mixed-species fisheries and could be adapted for other fisheries facing similar bycatch challenges.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".