Population structure and genetic diversity of<i>Magnolia cubensis</i>subsp.<i>acunae</i>(Magnoliaceae): effects of habitat fragmentation and implications for conservation
Bibliographic record
Abstract
Abstract Genetic data on threatened plant populations can facilitate the development of adequate conservation strategies to reduce extinction risk. Such data are particularly important for species affected by habitat fragmentation such asMagnolia cubensissubsp.acunae, a Critically Endangered magnolia subspecies endemic to Cuba. Using genetic data from 67 individuals, we aimed to evaluate the effect of habitat fragmentation on two subpopulations in the Guamuhaya mountain range, in Topes de Collantes Protected Natural Landscape and Lomas de Banao Ecological Reserve. We characterize the structure and genetic diversity of these subpopulations, with the objective of managing their conservation more effectively. We used Landsat satellite images to determine land-cover types at the two locations and calculated indices of habitat fragmentation. For genetic analyses, we extracted DNA from the leaf tissue of individuals from the two subpopulations and used 11 microsatellite markers to genotype them. We calculated heterozygosity, allelic richness and theF-statistics, to evaluate genetic variability. The montane rainforest in Topes de Collantes was most affected by habitat fragmentation, with smaller patches of more irregular shapes, compared to submontane forest at this location and both montane and submontane forests in Lomas de Banao. Genetic diversity was higher in Topes de Collantes, but we found no genetic differentiation between subpopulations. Our findings suggest the two subpopulations can be considered a single evolutionary unit and conservation entity. We propose to use individuals from both subpopulations for reinforcement to increase the overall genetic diversity of the subspecies.
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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 | 0.000 |
| 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".