Do rare pines need different conservation strategies? Evidence from three Mexican species
Bibliographic record
Abstract
Mexico is a major center of diversity for the genus Pinus as it has the greatest number of species in the world. Many species are now restricted to Mexico, and some are represented by very small populations and are in danger of extinction. In this study we examined allozyme variation in three rare species of Mexican pines: Pinus pinceana Gord., Pinus lagunae M.F. Passini, and Pinus muricata D. Don, with the objective of providing conservation guidelines. The three species had relatively high levels of genetic variation with mean expected heterozygosities of 0.373, 0.386, and 0.346 for P. pinceana, P. lagunae, and P. muricata, respectively. We found marked differentiation among populations and significant inbreeding within populations of the three species. These values are larger than the range reported for most conifers and suggest that conservation strategies of these rare pines require focusing on the viability of several populations. Given that our knowledge about the demographic status of the three species is scarce, we propose a mixed strategy of conservation. For P. lagunae, we propose an in situ strategy, whereas for P. pinceana and P. muricata we propose an ex situ strategy of conservation until permanent protection can be provided for several of their populations.Key words: genetic structure, conservation, rare pines, Pinus pinceana, Pinus lagunae, Pinus muricata.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".