High genetic drift in endangered northern peripheral populations of the Behr's hairstreak butterfly ( <scp> <i>Satyrium behrii</i> </scp> )
Bibliographic record
Abstract
Abstract The persistence and adaptation of leading‐edge peripheral populations may be critical for allowing species to shift their range limits under climate change. However, peripheral populations are potentially vulnerable to genetic, demographic, and environmental stochasticity. Here, we characterise genetic variation across space and among years in northern peripheral populations of the Behr's hairstreak butterfly in British Columbia, Canada. This butterfly is dependent on antelope‐brush ecosystems that are threatened in this part of the world and is federally listed as Endangered in Canada. Using a large panel of amplified fragment length polymorphism (AFLP) genetic markers, we found low diversity in these populations. We also detected a high degree of year‐to‐year variation in allele frequencies, resulting in low effective population size estimates. Our findings suggest that Canadian populations of the Behr's hairstreak experience high genetic drift and may be vulnerable to genetic stochasticity. Unstable demography, low effective population size, and low genetic diversity in these populations could impede their adaptation to rapidly changing environmental conditions and contribute to a contraction of the species' range under climate change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".