Congruent morphological and genetic differentiation as a signature of range expansion in a fragmented landscape
Bibliographic record
Abstract
Phenotypic differentiation is often interpreted as a result of local adaptation of individuals to their environment. Here, we investigated the skull morphological differentiation in 11 populations of the white-footed mouse (Peromyscus leucopus). These populations were sampled in an agricultural landscape in the Montérégie region (Québec, Canada), at the northern edge of the distribution of the white-footed mouse. We found a strong pattern of phenotypic differentiation matching the genetic structure across these populations. Landscape fragmentation and the presence of geographic barriers, in particular north-south oriented rivers, contribute to this differentiation and modulate the pattern of rapid ongoing northward range expansion of the white-footed mouse in response to climate warming. We conclude that while large rivers and postglacial recolonization routes have shaped the current pattern of distribution and differentiation of white-footed mouse populations, further local differentiation is occurring, at the scale of the landscape. We posit that the northern expansion of the white-footed mouse is achieved through successive independent founder events in a fragmented landscape at the northern range edge of the species. The phenotypic differentiation we observe is thus a result of a number of mechanisms operating at different spatial and temporal scales.
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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 | 0.000 | 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) | 0.001 | 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".