Ecological and evolutionary insights into the diversification of Atlantic bluefin tuna
Bibliographic record
Abstract
SUMMARY Identifying and preserving biological diversity is fundamental for the management of wild populations. The Atlantic bluefin tuna ( Thunnus thynnus ; ABT) is a vital species to the North Atlantic Ocean ecosystem that is now rebounding from decades of overfishing. This teleost fish is notable for its large body size, unique form of endothermy, and trans-oceanic migrations that enable individuals to move rapidly between spawning and foraging locations. Here we combine high-resolution whole genome sequencing data with spatial and environmental data from electronic tagging to improve our understanding of population structure in ABT. We analyzed whole genome sequences (n=82) from both larvae and adult fish representing the two recognized stocks (western and eastern) of ABT, which originate from geographically distinct spawning grounds. Analyzing these data, we identified 11,181,223 single nucleotide polymorphisms (SNPs), a dataset of unprecedented size and resolution. Coverage across the entire genome resulted in increased power in analyses of population structure, patterns of selection, and demographic history between the two recognized stocks. Notably, we report that both neutral and putatively adaptive SNPs are differentiated between populations, and through analyses of F ST outlier SNPs, we discovered candidate genes with potentially adaptive roles. We suggest that both demographic history and oceanographic variation of the spawning grounds have contributed to shaping bluefin tuna genomic diversity. Our results characterize adaptive variation that will be consequential for management decisions and critical for preserving locally adapted populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".