Spatial genetic patterns of a long-lived tree species: the case of <i>Pinus leiophylla</i> in a human-altered landscape of central Mexico
Bibliographic record
Abstract
In long-lived species, deforestation can modify the genetic diversity patterns of recent populations. The spatial-structure genetics of Pinus leiophylla Schiede ex Schltdl. & Cham. was investigated in adults and seedlings of remnant populations from central Mexico. Two chloroplast intergenic regions were sequenced of two cohorts (adults and seedlings) of 121 individuals of P. leiophylla from three localities. Higher genetic differentiation was found in adults ( ФST = 0.09) than in seedlings ( ФST = 0.03). The discriminant analysis of principal components detected four genetic clusters and the Mantel test found a weak but significant isolation by distance pattern ( r2 = 0.05, p = 0.002). Sixteen percent of genetic variation was explained by the spatial component according to the genetic neighborhood analysis. And the effect of three landscape features, i.e., elevation, aspect (sine and cosine), and land use, explored with ResistanceGA, detected that land use significantly restricts gene flow between populations, mostly in the farming category. This study emphasizes the critical role of preserving forest cover to maintain connectivity among remnants of P. leiophylla. Insights of this study will contribute to the conservation of conifers in Mexican temperate forests, especially within landscapes heavily altered by human activities.
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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 | 0.000 | 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) | 0.000 | 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".