Global habitat predictions to inform spatiotemporal fisheries management: Initial steps within the framework
Bibliographic record
Abstract
Tuna Regional Fishery Management Organizations (tRFMOs) are increasingly interested in spatiotemporal management as a tool to reduce interaction rates with vulnerable species. We use blue shark ( Prionace glauca ) as a case study to demonstrate the critical first steps in the implementation process, highlighting how predictions of global habitat for vulnerable life stages can be transformed into a publicly-accessible spatial bycatch mitigation tool. By providing examples of possible management goals and an associated threshold to identify essential habitats, we show how these key areas can represent a relatively low percentage of oceanic area on a monthly basis (16–24% between 50°S and 60°N), yet can have relatively high potential protection efficiency (∼ 42%) for vulnerable stages if fishing effort is redistributed elsewhere. While spatiotemporal management has demonstrable potential for blue sharks to effectively mitigate fishing mortality on sensitive life stages, we identify inherent challenges and sequential steps that require careful consideration by tRFMOs as work proceeds. We also discuss how our single-species framework could be easily extended to a multispecies approach by assigning relative conservation risk before layering habitat model predictions in an integrated analysis. Such broader application of our approach could address the goals of tRFMOs related to reducing the ecosystem effects of fishing and pave the way for efficient fisheries co-management using an ecosystem-based approach.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".