Leveraging genomic load estimates to optimize captive breeding programmes
Bibliographic record
Abstract
Rapid biodiversity loss threatens many species with extinction. Captive populations of species of conservation concern (such as those housed in zoos and dedicated breeding centres) act as an insurance should wild populations go extinct or need supplemental individuals to boost populations. Limited resources mean that captive populations are almost always small and started from few founding individuals. As a result, captive populations require careful management to minimize negative genetic impacts, with decisions about which individuals to breed together often guided by the principle of minimizing relatedness. Typically this strategy aims to retain 90% of genetic diversity over 200 years (Soulé et al., Zoo Biology, 1986, 5, 101), but it has a weakness in that it does not directly manage for genetic load. In this issue of Molecular Ecology Resources, Speak et al. (Molecular Ecology Resources, 2024, e13967) present a novel proof-of-concept study for taking this next step and incorporating estimates of individual genetic load into the planning of captive breeding, using an approach that is likely to be widely applicable to many captive populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".