Effects of temperature on global patterns of tuna and billfish richness
Bibliographic record
Abstract
Although tunas and billfishes are of substantial economic importance and conservation concern, global patterns of diversity and distribution remain poorly understood.Many species are highly migratory and able to tolerate a wide thermal range.In the present study, ambient water temperature data for 18 species of tuna and billfish from 190 literature sources were combined according to geographical location.An empirical modelling approach was used to relate temperature tolerances of tunas and billfishes to their global diversity patterns.Mean preferred and tolerated temperature ranges were calculated for each species in the adult and juvenile life stages.Mean tolerance data were then overlaid in order to fit models relating the species richness of tunas and billfishes to ambient water temperature.The best-fit model was used in conjunction with gridded water temperature data to predict global species richness patterns.Cumulative species richness predictions from water temperature data were positively correlated with observed longline-derived richness data (r = 0.577, p < 0.0001).Diversity consistently peaked at intermediate latitudes (10 to 35° N and S) in a manner similar to other pelagic taxa.This analysis provides evidence that the ambient water temperature tolerances of tunas and billfishes can be used to predict broad species richness patterns on a global scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".