Absence of founder effect and evidence for adaptive divergence in a recently introduced insular population of white‐tailed deer (<i>Odocoileus virginianus</i>)
Bibliographic record
Abstract
Abstract Islands are generally colonized by few individuals which could lead to a founder effect causing loss of genetic diversity and rapid divergence by strong genetic drift. Insular conditions can also induce new selective pressures on populations. Here, we investigated the extent of genetic differentiation within a white‐tailed deer (Odocoileus virginianus) population introduced on an island and its differentiation with its source mainland population. In response to their novel environmental conditions, introduced deer changed phenotypically from mainland individuals, therefore we investigated the genetic bases of the morphological differentiation. The study was conducted on Anticosti Island (Québec, Canada) where 220 individuals were introduced 120 years ago, resulting in a population size over 160,000 individuals. We used genotyping‐by‐sequencing (GBS) to generate 8,518 filtered high‐quality SNPs and compared patterns of genetic diversity and differentiation between the continental and Anticosti Island populations. Clustering analyses indicated a single panmictic island population and no sign of isolation by distance. Our results revealed a weak, albeit highly significant, genetic differentiation between the Anticosti Island population and its source population (mean FST = 0.005), which allowed a population assignment success of 93%. Also, the high genetic diversity maintained in the introduced population supports the absence of a strong founder effect due to the large number of founders followed by rapid population growth. We further used a polygenic approach to assess the genetic bases of the divergent phenotypical traits between insular and continental populations. We found loci related to muscular function and lipid metabolism, which suggested that these could be involved in local adaptation on Anticosti Island. We discuss these results in a harvest management context.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".