MHC variation reflects the bottleneck histories of <scp>N</scp>ew <scp>Z</scp>ealand passerines
Bibliographic record
Abstract
Most empirical evidence suggests that balancing selection does not counter the effects of genetic drift in shaping postbottleneck major histocompatibility complex (MHC) genetic diversity when population declines are severe or prolonged. However, few studies have been able to include data from historical specimens, or to compare populations/species with different bottleneck histories. In this study, we examined MHC class II B and microsatellite diversity in four New Zealand passerine (songbird) species that experienced moderate to very severe declines. We compared diversity from historical samples (collected c. 1884-1938) to present-day populations. Using a Bayesian framework, we found that the change in genetic diversity from historical to contemporary samples was affected by three main factors: (i) whether the data were based on MHC or microsatellite markers, (ii) species (as a surrogate for bottleneck severity) and (iii) whether the comparison between historical and contemporary samples was made using historical samples originating from the mainland, or using historical samples originating from islands. The greatest losses in genetic diversity occurred for the most severely bottlenecked species, particularly between historical mainland and contemporary samples. Additionally, where loss of diversity occurred, the change was greater for MHC genes compared to microsatellite loci.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".