Demography and environment modulate the effects of genetic diversity on extinction risk in a butterfly metapopulation
Bibliographic record
Abstract
Linking genetic diversity to extinction is a common goal in genomic studies. Recently, a debate has arisen regarding the importance of genetic variation in conservation as some studies have failed to find associations between genome-wide genetic diversity and extinction risk. However, only rarely are genetic diversity and fitness measured together in the wild, and typically demographic history and environment are ignored. It is therefore difficult to infer whether a lack of an association is real or obscured by confounding factors. To address these shortcomings, we analyzed genetic data from 7,501 individuals with extinction data from 279 meadows and mortality of 1,742 larval nests in a butterfly metapopulation. We found a strong negative association between genetic diversity and extinction when considering only heterozygosity in models. However, this association disappeared when accounting for ecological covariates, suggesting a confounding between demography and genetics and a more complex role for heterozygosity in extinction risk. Modeling interactions between heterozygosity and demographic variables revealed that associations between extinction and heterozygosity were context-dependent. For example, extinction declined with increasing heterozygosity in large, but not currently small populations, although negative associations between heterozygosity, extinction, and mortality were detected in small populations with a recent history of decline. We conclude that low genetic diversity is an important predictor of extinction, predicting >25% increase in extinction beyond ecological factors in certain contexts. These results highlight that inferences about the importance of genetic diversity for population viability should not rely on genomic data alone but require investments in obtaining demographic and environmental data from natural populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".