Genotyping on the ark: A synthesis of genetic resources available for species in zoos
Bibliographic record
Abstract
Using molecular genetic information to guide population management can improve the sustainability of species in captivity. However, empirical population genetics has not been commonly applied to species management programs in zoos. One limitation may be the availability of genetic resources (e.g., markers, primers, etc.) for species held in zoos. To assess the extent to which species held in zoos have been studied using population genetics in the wild, we conducted a systematic literature review of close to 8,000 papers. We synthesized information on the availability and scale of population genetics studies across amphibian, bird, mammal, and reptile species held in zoos, and discussed their potential for informing ex situ management. We found that more than half of the species in zoos (52%) already have some genetic markers described in the literature specific for them, or a congeneric species, that could be further developed to aid the management of zoo populations, and the accumulation of these resources has been steady over the past decades. Furthermore, the proportion of species with genetic resources is even higher (62%) for species that are being managed through a formal breeding program in zoos. Our study provides encouraging results for captive program managers interested in integrating population genetics into ex situ management strategies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".