Evolutionary stability, landscape heterogeneity, and human land-usage shape population genetic connectivity in the Cape Floristic Region biodiversity hotspot
Bibliographic record
Abstract
As human-induced change eliminates natural habitats, it impacts genetic diversity and population connectivity for local biodiversity. The South African Cape Floristic Region (CFR) is the most diverse extratropical area for plant biodiversity, and much of its habitat is protected as a UNESCO World Heritage site. There has long been great interest in explaining the underlying factors driving this unique diversity, especially as much of the CFR is endangered by urbanization and other anthropogenic activity. Here, we use a population and landscape genetic analysis of SNP data from the CFR endemic plant Leucadendron salignum or “common sunshine conebush” as a model to address the evolutionary and environmental factors shaping the vast CFR diversity. We found that high population structure, along with relatively deeper and older genealogies, are characteristic of the southwestern CFR, whereas, low population structure and more recent lineage coalescence depicts the eastern CFR. Population network analyses show genetic connectivity is facilitated in areas of lower elevation and higher seasonal precipitation. These population genetic signatures corroborate CFR species-level patterns consistent with high Pleistocene biome stability and landscape heterogeneity in the southwest, but with coincident instability in the east. Finally, we also find evidence of human land-usage as a significant gene flow barrier, especially in severely-threatened lowlands where genetic connectivity has been historically the highest. These results help identify areas where conservation plans can prioritize protecting high genetic diversity threatened by contemporary human activities within this unique cultural UNESCO site.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".