Macrogenetics reveals multifaceted influences of environmental variation on vertebrate population genetic diversity across the Americas
Bibliographic record
Abstract
Abstract Relative to species diversity gradients, the broad scale distribution of population-specific genetic diversity (PGD) across taxa remains understudied. We used nuclear DNA data collected from 6285 vertebrate populations across the Americas to assess the role environmental variables play in structuring the spatial/latitudinal distribution of PGD, a key component of adaptive potential in the face of environmental change. Our results provide key evidence for taxa-specific responses and that temperature variability in addition to mean temperature may be a primary driver of PGD. Additionally, we found some positive influence of precipitation, productivity, and elevation on PGD; identified trends were dependent on the metric of PGD. In contrast to the classic negative relationship between species diversity and latitude, we report either a positive or taxa-dependent relationship between PGD and latitude, depending on the metric of PGD. The inconsistent latitudinal gradient in different metrics of PGD may be due to opposing processes diminishing patterns across latitudes that operate on different timescales, as well as the flattening of large-scale genetic gradients when assessing across species versus within species. Our study highlights the nuance required to assess broad patterns in genetic diversity, and the need for developing balanced conservation strategies that ensure population, species, and community persistence.
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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) | 0.001 | 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".