The influence of habitat and landscape structure on the genetic differentiation of the white-footed mouse (Peromyscus leucopus)
Bibliographic record
Abstract
The white-footed mouse (Peromyscus leucopus) is a widespread habitat generalist species abundant over a large part of the North-American continent. In the past decade, due to climate and land use change, the range of this species has expanded northwards into Canada. The black-legged tick (Ixodes scapularis), is the vector of Lyme disease which also has tracked climate change over the last few decades. This may have been further promoted by the growing presence of P. leucopus, a favored host for the tick. Therefore, aspects of the landscape that affect the movement and distribution of the white-footed mouse, will also affect the expansion of the tick, and consequently the spread of Lyme disease. In this thesis, I first reviewed published results that relied on genetic and non- genetic biological data to investigate the influence of local habitat and landscape characteristics on the movement and dispersal patterns in the white footed-mouse. Next, I evaluated the relations between breeding habitat and landscape resistance against the genetic differentiation between 11 populations in Montérégie, Québec, Canada. I was able to simultaneously measure the effect of the habitat and the landscape on the genetic differentiation of these mouse populations by utilizing numerical optimization to fit a model to previously published genetic data. I used ecological distance computed from resistance surfaces with Circuitscape to infer the effect of the landscape. Concurrently, I estimated the habitat quality of our sampling localities and correlated these to relevant habitat measurements. I found that both characteristics within and between forest patches have more of an impact on genetic differentiation than the geographical distance between the mice populations. This suggests that this species can disperse and use a wide range of habitats, in accordance with its recent rapid expansion in the region.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".