Determinants of Population Genetic Structure in Eastern Chipmunks (Tamias striatus): The Role of Landscape Barriers and Sex-Biased Dispersal
Bibliographic record
Abstract
Dispersal and gene flow are important processes affecting the evolutionary potential of wild populations. Assessing the importance of such patterns is thus critical, especially in contexts where environmental attributes may enhance or restrict the movements of individuals across patchy habitats. A landscape genetics approach is effective in that respect as it combines spatial and genetic data to identify landscape features that play a role in shaping genetic structure. The primary objective of our research was to characterize the determinants of population genetic structure in the eastern chipmunk (Tamias striatus) over a large heterogeneous study area in southern Quebec and Ontario, Canada. We genotyped 572 individuals using 7 microsatellites loci and found an average F(ST) of 0.127 +/- 0.035 among our 7 sampling sites. We found evidence that major rivers act as important barriers to gene flow at a large scale. We also detected a signal of male-biased gene flow at all scales considered. Our findings highlight the importance of simultaneously taking into account landscape elements and geographic distance, considering the scale at which determinants of genetic structure may act and using the appropriate measures to detect sex-biased dispersal based on the characteristics of the sampling design.
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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 | 0.000 |
| 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".