Determinants of genetic diversity and species richness of North American amphibians
Bibliographic record
Abstract
Abstract Ecological limits on the population sizes and number of species a region is capable of supporting are thought to simultaneously produce spatial patterns in genetic diversity and species richness. However, we do not know the extent to which resource-based environmental limits jointly determine these patterns of biodiversity in ectotherms because of their low energy requirements compared to endotherms. Here, we adapt a framework for the ways ecological limits may shape genetic diversity and species richness previously tested in mammals for amphibians to determine whether similar processes produce continental patterns of biodiversity across both taxa. Repurposing open, raw microsatellite data from 19 species sampled at 554 sites in North America we found that spatial patterns of genetic diversity run opposite to patterns of species richness and genetic differentiation. However, while measures of resource availability and niche heterogeneity predict 89% of the variation in species richness, these landscape metrics are poor predictors of genetic diversity. Although heterogeneity appears to be an important driver of genetic and species biodiversity patterns in both amphibians and mammals, our results suggest that variation in genetic diversity both within and across species makes it difficult to infer general processes producing spatial patterns of amphibian 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".