Latitudinal genetic diversity gradients steepen toward the poles
Bibliographic record
Abstract
Latitudinal biodiversity gradients are among the best-described biogeographic patterns. However, there is little agreement on whether genetic diversity, the most fundamental level of biodiversity, is also latitudinally distributed. The confusion about the distribution of genetic diversity at biogeographic scales stems in part from the fact that genetic diversity gradients have been described for multiple types of genetic diversity, with good reasons to expect patterns to vary with the component of genetic diversity examined. Genome-wide diversity varies both within and across species. Thus, nuclear genetic diversity gradients might arise due to the existence of parallel latitudinal gradients within species, or due to species turnover and differences in species-level genetic diversity across latitudes. We used a compilation of nuclear genetic data from 100 mammal species across 1,426 locations to test for latitudinal genetic diversity gradients using Bayesian hierarchical regressions. We detected no general latitudinal genetic diversity gradients within or across species. However, the direction of within-species genetic diversity gradients was associated with species attributes. Notably, the slopes of intraspecific latitudinal gradients became increasingly positive for species distributed at higher latitudes. Interactions between species-level and population-level processes appear to shape the biogeography of genetic diversity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".