Isolation by adaptation in Neochlamisus leaf beetles: host-related selection promotes neutral genomic divergence
Bibliographic record
Abstract
This study tests how divergent natural selection promotes genomic differentiation during ecological speciation. Specifically, we use adaptive ecological divergence (here, population divergence in host plant use and preference) as a proxy for selection strength and evaluate the correlation between levels of adaptive and genetic differentiation across pairwise population comparisons. Positive correlations would reveal the pattern predicted by our hypothesis, that of 'isolation by adaptation' (IBA). Notably, IBA is predicted not only for selected loci but also for neutral loci. This may reflect the effects of divergent selection on neutral loci that are 'loosely linked' to divergently selected loci or on geneflow restriction that facilitates genetic drift at all loci, including neutral loci that are completely unlinked to those evolving under divergent selection. Here, we evaluate IBA in maple- and willow-associated populations of Neochlamisus bebbianae leaf beetles. To do so, we collected host preference data to construct adaptive divergence indices and used AFLPs (amplified fragment length polymorphisms) and mitochondrial sequences to quantify genetic differentiation. Partial Mantel tests showed significant IBA in 'pooled' analyses of putatively neutral and of putatively selected ('outlier') AFLP loci. This pattern was also recovered in 12% of 'locus-specific' analyses that separately evaluated genetic differentiation at individual neutral loci. These results provided evidence for widespread effects of selection on neutral genomic divergence. Our collective findings indicate that host-related selection may play important roles in the population genomic differentiation of both neutral and selected gene regions in herbivorous insects.
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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".