Conservation genetics of an endangered grassland butterfly ( <i>Oarisma poweshiek</i> ) reveals historically high gene flow despite recent and rapid range loss
Bibliographic record
Abstract
Abstract In poorly dispersing species gene flow can be facilitated when suitable habitat is widespread, allowing for increased dispersal between neighbouring locations. The Poweshiek skipperling [ Oarisma poweshiek (Parker)], a federally endangered butterfly, has undergone a rapid, recent demographic decline following the loss of tallgrass prairie and fen habitats range wide. The loss of habitat, now restricted geographic range, and poor dispersal ability have left O. poweshiek at increased risk of extinction. We studied the population genetics of six remaining populations of O. poweshiek in order to test the hypothesis that gene flow was historically high despite limited long‐distance dispersal capability. Utilising nine microsatellite loci developed by PacBio sequencing, we tested for patterns of isolation by distance, low population genetic structure and alternative gene flow models. Populations from southern Manitoba, Canada to the Lower Peninsula of Michigan, USA are only weakly genetically differentiated despite having low diversity. We found no support for isolation by distance, and Bayesian estimates of historical gene flow support our hypothesis that high levels of gene flow previously connected populations from Michigan to Wisconsin. Prairie grasslands have been reduced tremendously over the past century, but the low mobility of O. poweshiek suggests that rapid loss of populations over the past decade cannot be simply explained by fragmentation of habitat. As a species at high risk of extinction, understanding historical processes of gene flow will allow for informed management decisions with respect to head‐starting individuals for population reintroductions and for conserving networks of habitat that will allow for high levels of gene flow.
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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 | 0.000 | 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) | 0.001 | 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".