Multi-level patterns of genetic structure and isolation by distance in the widespread plant Mimulus guttatus
Bibliographic record
Abstract
An understanding of genetic structure is essential for answering many questions in population genetics. However, complex population dynamics and scale-dependent processes can make it difficult to detect if there are distinct genetic clusters present in natural populations. Inferring discrete population structure is particularly challenging in the presence of continuous genetic variation such as isolation by distance. Here, we use the plant species Mimulus guttatus as a case study for understanding genetic structure at three spatial scales. We use reduced-representation sequencing and marker-based genotyping to understand dispersal dynamics and to characterise genetic structure. Our results provide insight into the spatial scale of genetic structure in a widespread plant species, and demonstrate how dispersal affects spatial genetic variation at the local, regional, and range-wide scale. At a fine-spatial scale, we show dispersal is rampant with little evidence of spatial genetic structure within populations. At a regional-scale, we show continuous differentiation driven by isolation by distance over hundreds of kilometres, with broad geographic genetic clusters that span major barriers to dispersal. Across Western North America, we observe geographic genetic structure and the genetic signature of multiple postglacial recolonisation events, with historical gene flow linking isolated populations. Our genetic analyses show M. guttatus is highly dispersive and maintains large metapopulations with high intrapopulation variation. This high diversity and dispersal confounds the inference of genetic structure, with multi-level sampling and spatially-explicit analyses required to understand population history.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".