Colonization of the Beaver Island Archipelago by deer mice (<i>Peromyscus</i><i> maniculatus</i> <i>gracilis</i>): mtDNA evidence for multiple origins
Bibliographic record
Abstract
The Beaver Island group in Lake Michigan comprises nine islands ranging from 0.3 to 144 km2, lying approximately 30 km west of Michigan’s Lower Peninsula (LP) and 15 km south of the Upper Peninsula (UP). The islands have been isolated from mainland Michigan for most of their postglacial history, but were connected to the mainland LP during the low-water Chippewa stage (ending about 7400 years before present (YBP)). Although plants and animals could have colonized the islands during the Chippewa period, flooding during the subsequent Nipissing high-water stand means that the smaller islands have been colonized more recently. We analyzed 481 bp of mitochondrial D-loop sequences from woodland deer mice (Peromyscus maniculatus (Wagner, 1845)) on six of these islands in northern Lake Michigan to elucidate the mainland origin and minimum number of colonization events for this species. Surprisingly, the distribution of haplotypes on the islands suggests that the populations on most islands likely had separate recent origins on the mainland UP. Approximate Bayesian computation supports a scenario in which individual islands were colonized separately by distinct groups of mice. Together, the data suggest multiple colonization events from the UP, rather than expansion from a bottlenecked population or a single colonization event.
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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 | 0.000 |
| 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".