Genetic Approaches Refine Ex Situ Lowland Tapir (Tapirus terrestris) Conservation
Bibliographic record
Abstract
Ex situ conservation management remains an important tool in the face of continued habitat loss and global environmental change. Here, we use microsatellite marker variation to evaluate conventional assumptions of pedigree-based ex situ population management and directly inform a captive lowland tapir breeding program within a range country. We found relatively high levels of genetic variation (Ntotal = 41; mean HE = 0.67 across 10 variable loci) and little evidence for relatedness among founder individuals (Nfounders = 10; mean relatedness = −0.05). Seven of 29 putative parent–offspring relationships were excluded by parentage analysis based on allele sharing, and we identified 2 individuals of high genetic value to the population (mk ≤ 0.007) that would otherwise have been excluded from the breeding program. Traditional assumptions of founders being unrelated and individuals of unknown origin being highly related led to overestimates of mean kinship and inbreeding, and underestimates of gene diversity, when compared with values found when genetic markers were used to inform kinship. We discuss our results within the context of recent studies that have assessed the utility of neutral molecular markers for ex situ conservation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".