Determinants of genetic diversity and species richness of North American amphibians
Bibliographic record
Abstract
Abstract Aim Ecological limits on population sizes and the number of species a region can sustain are thought to simultaneously produce spatial patterns in population genetic diversity and species richness due to the effects of random drift operating in parallel across population and community levels. Here, we test the extent to which resource‐based environmental limits jointly determine these patterns of biodiversity in amphibians. Location North America. Taxon Amphibians. Methods We repurposed open, raw microsatellite data from 19 species sampled at 554 sites in North America and mapped nuclear genetic diversity at the continental scale. We then tested whether ecological limits defined by resource availability and environmental heterogeneity could simultaneously shape biogeographic patterns in genetic diversity and species richness with structural equation modelling. Results Spatial patterns of population genetic diversity run opposite 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 were poor predictors of genetic diversity. Main conclusions Although heterogeneity appears to be an important driver of genetic and species biodiversity patterns in amphibians, variation in genetic diversity both within and across species makes it difficult to infer general processes producing spatial patterns of amphibian genetic diversity. This result differs from those found in endotherms and may be due to the considerable life history variation found across amphibians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".