Genetic diversity and structure in Canadian northern leopard frog (<i>Rana pipiens</i>) populations: implications for reintroduction programs
Bibliographic record
Abstract
The northern leopard frog ( Rana pipiens Schreber, 1782) underwent a large decline in the western portion of its range and only occurs in 20% of historically occupied sites in Alberta. Its absence may reflect an inability to disperse to these sites because of habitat fragmentation, and human-mediated translocation has been proposed. In this study, we used three criteria to examine the genetic suitability of potential translocation sources: diversity, similarity to area of reintroduction, and evolutionary history. We genotyped 187 samples and sequenced 812 bp of the mitochondrial NADH dehydrogenase 1 gene from 14 Canadian northern leopard frog populations. Nuclear and mitochondrial diversity were highest in Manitoba and western Ontario and declined westward. There was no significant relationship between genetic and geographic distance, suggesting that genetic drift is a driving force affecting the genetic relationships between populations. Regions separated by more than ~50 km were quite differentiated. Therefore, source populations similar to the original inhabitants of an area for reintroduction may be uncommon. Mitochondrial analyses revealed that all populations share a close evolutionary history, belonging to the western haplotype group. While genetic criteria support the use of Manitoba and Ontario as sources, the desirability of environmental similarity to the reintroduction site suggests that ecologically exchangeable Alberta populations should also be considered.
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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 | 0.001 | 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".