Genetic management on the brink of extinction: sequencing microsatellites does not improve estimates of inbreeding in wild and captive Vancouver Island marmots (Marmota vancouverensis)
Bibliographic record
Abstract
Abstract Captive breeding is often a last resort management option in the conservation of endangered species which can in turn lead to increased risk of inbreeding depression and loss of genetic diversity. Thus, recording breeding events via studbook for the purpose of estimating relatedness, and facilitating mating pair selection to minimize inbreeding, is common practice. However, as founder relatedness is often unknown, loss of genetic variation and inbreeding cannot be entirely avoided. Molecular genotyping is slowly being adopted in captive breeding programs, however achieving sufficient resolution can be challenging in small, low diversity, populations. Here, we evaluate the success of the Vancouver Island marmot (Marmota vancouverensis; VIM; among the worlds most endangered mammals) captive breeding program in preventing inbreeding and maintaining genetic diversity. We explored the use of high-throughput amplicon sequencing of microsatellite regions to assay greater genetic variation in both captive and wild populations than traditional length-based fragment analysis. Contrary to other studies, this method did not considerably increase diversity estimates, suggesting: (1) that the technique does not universally improve resolution, and (2) VIM have exceedingly low diversity. Studbook estimates of pairwise relatedness and inbreeding in the current population were weakly, but positively, correlated to molecular estimates. Thus, current studbooks are moderately effective at predicting genetic similarity when founder relatedness is known. Finally, we found that captive and wild populations did not differ in allelic frequencies, and conservation efforts to maintain diversity have been successful with no significant decrease in diversity over the last three generations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".