Nuclear and cytoplasmic genetic diversity reveals long-term population decline in<i>Abies semenovii</i>, an endemic fir of central Asia
Bibliographic record
Abstract
The genus Abies is one of the largest conifer genera and many of the marginal species remain poorly characterized. Abies semenovii B. Fedtsch. is a rare mountain fir species from central Asia, and its species status is still disputed. We used both nuclear (allozymes and AFLP) and chloroplastic (cpSSR) markers to show that A. semenovii deserves to be considered as a species and that its low genetic diversity justifies more a proactive conservation policy. First, A. semenovii was significantly differentiated from the Siberian fir Abies sibirica Ledeb. and we did not detect gene flow between the two species. Second, A. semenovii has a very low nuclear genetic diversity, suggesting a prolonged restricted effective population size. Abies semenovii had low cpSSR diversity too but the identification of seven closely related haplotypes suggests that these mutations accumulated recently during a phase of population expansion. This agrees well with the palynological record and is in contrast with the situation observed in another rare Eurasian fir endemic to Kamchatka, Abies gracilis Kom., which was devoid of variation in cpSSRs but that also had a more substantial nuclear marker diversity than A. semenovii, thereby suggesting a more recent but less severe population bottleneck.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".