Variability, Drivers, and Utility of Genetic Diversity‐Area Relationships in Terrestrial Vertebrates
Bibliographic record
Abstract
Maintaining genetic diversity within and among populations is critical for conservation and a prominent goal of the Kunming-Montreal Global Biodiversity Framework. However, direct estimates of genetic diversity are unavailable for most species, and time and resources are insufficient to fill these substantial data gaps and meet conservation target timelines. We evaluated a proxy-based prediction of genetic diversity loss, the Genetic Diversity Area Relationship (GDAR), which describes relationships between genetic diversity and the geographic area occupied by a species. We estimated differences in three metrics of genetic diversity relative to sample area using 55 previously published datasets from 51 species. GDARs were highly variable across species and strongly dependent on population structure, with no clear differences across vertebrate classes. Traits correlated with population structure and study area explained 35%-45% of the variation in GDARs. Across genetic diversity metrics, prediction accuracy was highest for GDARs estimated from allele count compared to allelic richness and gene diversity. Our findings suggest there are opportunities for refining taxon-specific GDARs to predict genetic diversity loss following area loss in the absence of genetic data.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".