Whole-genome evaluation of genetic rescue: the case of a curiously isolated and endangered butterfly
Bibliographic record
Abstract
Abstract Genetic rescue, or the translocation of individuals among populations to augment gene flow, can help ameliorate inbreeding depression and loss of adaptive potential in small and isolated populations. Genetic rescue is currently under consideration for an endangered butterfly in Canada, the half-moon hairstreak ( Satyrium semiluna ). A small, unique population persists in Waterton Lakes National Park, Alberta, isolated from other populations by >350km. However, whether genetic rescue would actually be helpful has not been evaluated. Here, we generate the first chromosome-level genome assembly and whole-genome resequence data for the species. We find that the Alberta population’s genetic diversity is extremely low and very divergent from the nearest populations in British Columbia and Montana. Runs of homozygosity suggest this is due to a long history of inbreeding, and coalescent analyses show that the population has been small and isolated, yet stable for up to 40k years. When a population maintains its viability despite inbreeding and low genetic diversity, it has likely undergone purging of deleterious recessive alleles and could be threatened by their reintroduction via genetic rescue. Ecological niche modelling indicates that the Alberta population also exhibits environmental associations that are atypical of the species. Together, these results suggest that population crosses are likely to result in outbreeding depression. We infer that, in this case, genetic rescue has a relatively unique potential to be harmful rather than helpful at present. However, due to reduced adaptive potential, the Alberta may still benefit from future genetic rescue as climate conditions change. Proactive experimental population crosses should be completed to assess reproductive compatibility and offspring fitness.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".