Incorporating Genetic Diversity to Optimize the Plant Conservation Network in the Third Pole
Bibliographic record
Abstract
ABSTRACT Climate change poses a significant threat to the survival of many species. Although protected areas can slow down biodiversity loss, they often lack systematic planning and do not integrate genetic diversity. Genetic diversity is a key prerequisite for species survival and the ability to tolerate new conditions. Using population genetic and distribution data from 96 plant species in the Third Pole (encompassing the Tibetan Plateau and adjacent mountains), we mapped patterns of genetic diversity, projected climate‐driven range dynamics and future genetic erosion, and designed an optimal conservation framework for the region. We identified several patches of high haplotype diversity ( H D ), with a relatively high number of haplotypes in southeastern Third Pole. Regression models revealed that climate and topography have interacted to shape patterns of genetic diversity, with latitude and precipitation being the best predictors for H D of cpDNA and nrDNA, respectively. Ecological niche modeling predicted an approximate 43 km northwestward and 86 m upward shift in suitable habitats under future climate scenarios, likely leading to a significant loss of up to 13.19% and 15.49% of cpDNA and nrDNA genetic diversity, respectively. Alarmingly, 71.20% of the newly identified conservation priority areas fall outside of the existing protected areas and planned National Park Clusters. Therefore, we recommend expanding the network by 2.02 × 10 5 km 2 (5.91%) in the Third Pole, increasing the total conserved area to 1.36 × 10 6 km 2 (39.93%) to effectively preserve the evolutionary potential of plants. This study represents an innovative attempt to incorporate genetic diversity into biodiversity conservation efforts.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".