Continued collaboration of ex situ and in situ programs is critical for the genetic sustainability of the endangered Rana pretiosa
Bibliographic record
Abstract
Abstract Retaining sufficient genetic variation for both short and long-term sustainability is a chief aim of ex situ programs for threatened species. Conservation breeding and reintroduction programs exist but oftentimes little is known about the genetic variation of in situ or ex situ populations. We collected genetic samples from both wild and zoo populations of Canada’s most endangered anuran, the Oregon Spotted Frog (Rana pretiosa) to compare genetic diversity (observed and expected heterozygosity), inbreeding coefficients (FIS), effective population sizes (Ne) and population structure using single-nucleotide polymorphisms (SNPs). We found low diversity in situ and lower diversity ex situ, with positive inbreeding coefficients indicating assortative mating in both wild and zoo populations. Ex situ breeding programs that allowed free mate choice retained more genetic variation compared to those where breeding groups were pre-determined. Mixed source zoo populations were less differentiated from their wild source populations than the latter were among themselves, indicating sufficient representation of wild populations in zoo populations. The patterns we uncover support continued collaboration of ex situ and in situ endeavours as supplementation will likely be required for the long-term viability of the very wild populations the zoos rely on for genetic sustainability.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".