Bibliographic record
Abstract
Is a key theory of evolutionary and conservation biology-that loss of genetic diversity can be predicted from population size-on shaky ground? In the face of increasing human-induced species depletion and habitat fragmentation, this question and the study of genetic diversity in small populations are paramount to understanding the limits of species' responses to environmental change and to providing remedies to endangered species conservation. Few empirical studies have investigated to what degree some small populations might be buffered against losses of genetic diversity. Even fewer studies have experimentally tested the potential underlying mechanisms. The study of Schou, Loeschcke, Bechsgaard, Schlotterer, and Kristensen () in this issue of Molecular Ecology is elegant in combining classic common garden experimentation with population genomics on an iconic experimental model species (Drosophila melanogaster). The authors reveal a slower rate of loss of genetic diversity in small populations under varying thermal regimes than theoretically expected and hence an unexpected retention of genetic diversity. They are further able to hone in on a plausible mechanism: associative overdominance, wherein homozygosity of deleterious recessive alleles is especially disfavoured in genomic regions of low recombination. These results contribute to a budding literature on the varying mechanisms underlying genetic diversity in small populations and encourage further such research towards the effective management and conservation of fragmented or endangered 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".