Temporal stability and assignment power of adaptively divergent genomic regions between herring (<i>Clupea harengus</i>) seasonal spawning aggregations
Bibliographic record
Abstract
Abstract Atlantic herring (Clupea harengus), a vital ecosystem component and target of the largest Northwest Atlantic pelagic fishery, undergo seasonal spawning migrations that result in elusive sympatric population structure. Herring spawn mostly in fall or spring, and genomic differentiation was recently detected between these groups. Here we used a subset of this differentiation, 66 single nucleotide polymorphisms (SNPs) to analyze the temporal dynamics of this local adaptation and the applicability of SNP subsets in stock assessment. We showed remarkable temporal stability of genomic differentiation corresponding to spawning season, between samples taken a decade apart (2005 N = 90 vs. 2014 N = 71) in the Gulf of St. Lawrence, and new evidence of limited interbreeding between spawning components. We also examined an understudied and overexploited herring population in Bras d'Or lake (N = 97); using highly reduced SNP panels (NSNPs > 6), we verified little‐known sympatric spawning populations within this unique inland sea. These results describe consistent local adaptation, arising from asynchronous reproduction in a migratory and dynamic marine species. Our research demonstrates the efficiency and precision of SNP‐based assessments of sympatric subpopulations; and indeed, this temporally stable local adaptation underlines the importance of such fine‐scale management practices.
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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".