Population declines of tuna and relatives depend on their speed of life
Bibliographic record
Abstract
Larger-bodied species in a wide range of taxonomic groups including mammals, fishes and birds tend to decline more steeply and are at greater risk of extinction. Yet, the diversity in life histories is governed not only by body size, but also by time-related traits. A key question is whether this size-dependency of vulnerability also holds, not just locally, but globally across a wider range of environments. We test the relative importance of size- and time-related life-history traits and fishing mortality in determining population declines and current exploitation status in tunas and their relatives. We use high-quality datasets of half a century of population trajectories combined with population-level fishing mortalities and life-history traits. Time-related traits (e.g. growth rate), rather than size-related traits (e.g. maximum size), better explain the extent and rate of declines and current exploitation status across tuna assemblages, after controlling for fishing mortality. Consequently, there is strong geographical patterning in population declines, such that populations with slower life histories (found at higher cooler latitudes) have declined most and more steeply and have a higher probability of being overfished than populations with faster life histories (found at tropical latitudes). Hence, the strong, temperature-driven, latitudinal gradients in life-history traits may underlie the global patterning of population declines, fisheries collapses and local extinctions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".