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. Robust, proxy-based predictions of genetic diversity loss would therefore be valuable for conserving genetic diversity for the many species lacking DNA-based data. We evaluated one such approach, the Genetic Diversity Area Relationship (GDAR), which describes the relationship between genetic diversity and the geographic area occupied by a species. We estimated differences in genetic diversity relative to the size of sample area using 55 previously published datasets from 51 species and found that GDARs were highly variable across species and strongly dependent on population structure. The mean change in allele count relative to area sampled across all species did not predict genetic diversity differences for individual species well. Traits correlated with population structure explained little variation in the GDAR. Our findings suggest that using a single GDAR is not appropriate to predict genetic diversity loss for individual species following area loss. Further work is needed to identify accurate methods to estimate species-specific levels of genetic diversity decline with area without genetic data. Although the GDAR remains valuable to highlight likely global patterns and scales of genetic diversity loss across many species, our results suggest it is currently too inaccurate for species-specific use.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".