Climatic niche modeling reveals divergence between cytotypes in <i>Eutrema edwardsii</i> (Brassicaceae)
Bibliographic record
Abstract
Polyploidy among plants is most frequent in the arctic, where glaciation cycles put selective pressures on populations by repeated fragmentation and fluctuation in climate. Polyploids should have been more fit in the novel habitats created as glaciers receded because of increased genetic material and novel gene products, which results in phenotypic plasticity and rapid adaptation. Higher ploidy is then expected to confer a broader tolerance of environmental conditions. Eutrema edwardsii R. Br. (Brassicaceae) is an arctic-alpine mustard with a near circumpolar distribution that occurs as a tetraploid, hexaploid, and octaploid. We used flow cytometry to document the distribution of polyploid cytotypes using herbarium tissue, and modeled the niche of each cytotype to test for niche differentiation. Flow cytometry revealed four cytotypes among 85 individuals. Notably, 60% of the herbarium tissue assays were successful using tissue up to 50 years old. Principle components analysis was performed on 20 climatic variables, of which, the first four axes were used as environmental variables for niche modeling. Niche models were created for tetraploid and hexaploid populations and used to calculate niche overlap (Shoener’s D). Overlap between tetraploid and hexaploid models (D = 0.534) is lower than the null distribution (D = 0.681–0.944) supporting the hypothesis of niche divergence.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".