Predicting overfishing and extinction threats in multispecies fisheries
Bibliographic record
Abstract
Threats to species from commercial fishing are rarely identified until species have suffered large population declines, by which time remedial actions can have severe economic consequences, such as closure of fisheries. Many of the species most threatened by fishing are caught in multispecies fisheries, which can remain profitable even as populations of some species collapse. Here we show for multispecies fisheries that the biological and socioeconomic conditions that would eventually cause species to be severely depleted or even driven extinct can be identified decades before those species experience high harvest rates or marked population declines. Because fishing effort imposes a common source of mortality on all species in a fishery, the long-term impact of a fishery on a species is predicted by measuring its loss rate relative to that of species that influence the fishery's maximal effort. We tested our approach on eight Pacific tuna and billfish populations, four of which have been identified recently as in decline and threatened with overfishing. The severe depletion of all four populations could have been predicted in the 1950s, using our approach. Our results demonstrate that species threatened by human harvesting can be identified much earlier, providing time for adjustments in harvesting practices before consequences become severe and fishery closures or other socioeconomically disruptive interventions are required to protect species.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".