No founder effects observed in rapidly expanding Peromyscus leucopus populations in Michigan’s Upper Peninsula
Bibliographic record
Abstract
As species shift their distributions and establish new populations in response to climate change, maintaining connectivity with historical populations is important for genetic diversity and population viability. Populations of Peromyscus leucopus (Rafinesque, 1818) have been expanding into the Upper Peninsula (UP) of Michigan since the 1980’s, with the current range of P. leucopus extending more than 200 km from its original range in Wisconsin. To observe whether this rapid expansion of P. leucopus resulted in founder effects, five populations across the UP were sampled to determine if genetic or morphological variation was reduced in newer populations. The analyses produced three conclusions that were contrary to our predictions: the genetic structure of the UP populations sorted into two expansions (one from the west originating in Wisconsin, one from the east originating primarily in the Lower Peninsula), these expansions were supported by multiple introductions, and all populations across the UP were genetically well connected. Geometric skull shape was affected by the population of origin, even when accounting for differences in haplotype grouping. This robust genetic connectivity is likely bolstered by human-mediated transport, which has allowed P. leucopus to colonize the UP more quickly than would be possible by natural means.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".