Macrogenetics reveals multifaceted influences of environmental variation on vertebrate population genetic diversity across the Americas
Bibliographic record
Abstract
Abstract The broad scale distribution of population‐specific genetic diversity (GD P ) across taxa remains understudied relative to species diversity gradients, despite its relevance for systematic conservation planning. We used nuclear DNA data collected from 3678 vertebrate populations across the Americas to assess the role of environmental and spatial variables in structuring the distribution of GD P , a key component of adaptive potential in the face of environmental change. We specifically assessed non‐linear trends for a metric of GD P, expected heterozygosity ( H E ), and found more evidence for spatial hotspots and cold spots in H E rather than a strict pattern with latitude. We also detected inconsistent relationships between H E and environmental variables, where only 11 of 30 environmental comparisons among taxa groups were statistically significant at the .05 level, and the shape of significant trends differed substantially across vertebrate groups. Only one of six taxonomic groups, freshwater fishes, consistently showed significant relationships between H E and most (four of five) environmental variables. The remaining groups had statistically significant relationships for either two (amphibians, reptiles), one (birds, mammals), or no variables (anadromous fishes). Our study highlights gaps in the theoretical foundation upon which macrogenetic predictions have been made thus far in the literature, as well as the nuances for assessing broad patterns in GD P among vertebrate groups. Overall, our results suggest a disconnect between patterns of species and genetic diversity, and underscores that large‐scale factors affecting genetic diversity may not be the same factors as those shaping taxonomic diversity. Thus, careful spatial and taxonomic‐specific considerations are needed for applying macrogenetics to conservation planning.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".