Purifying selection purges harmful variants in the rarest pine
Bibliographic record
Abstract
Abstract Population genetic theory predicts that severe bottlenecks and extremely small effective population sizes ( N e ) should reduce the ability of natural selection to eliminate harmful mutations. Under this framework, deleterious alleles are expected to accumulate and even fix, eroding fitness, constraining evolutionary rescue, and potentially precipitating mutational meltdown. Yet, empirical tests of these predictions in species at the extreme lower bound of N e remain rare. We address this gap using Pinus squamata , one of the rarest tree species on Earth, with only 35 wild individuals remaining. We generated a near-complete reference genome (29.2 Gb) for this species and performed population genomic analyses across nearly all of its extant individuals, together with two closely related species. P. squamata exhibits extraordinarily low nucleotide diversity (π = 3.35 × 10⁻⁵), the lowest reported for any plant. Demographic inference reveals a recent and severe bottleneck (∼20 generations ago) that reduced N e to ∼2.7 and resulted in intense inbreeding. Contrary to theoretical expectations, we uncover evidence for highly efficient purifying selection: strongly deleterious mutations are markedly depleted, indicating substantial purging despite the extremely small N e . Genome-wide patterns further implicate selection at linked sites—including background selection and pseudo-overdominance—as dominant forces shaping genomic variation in the species. These results challenge the prevailing view that drift overwhelms selection in extremely small populations. Instead, they suggest that, under certain genomic and demographic conditions, purifying selection can remain unexpectedly effective, potentially mitigating the risk of mutational meltdown in the rarest species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".