Genetic variation in remnant <i>Festuca hallii</i> populations is weakly differentiated, but geographically associated across the Canadian Prairie
Bibliographic record
Abstract
Abstract Genetic diversity is essential for predicting plant evolutionary potential and for formulating conservation strategies. However, little is known about the genetic variation of plains rough fescue [ Festuca hallii (Vasey) Piper], a widespread and diagnostic grass in the Fescue Prairie. We used the amplified fragment length polymorphism (AFLP) technique to assess the genetic variation of 30 fragmented populations of F. hallii across the northern Canadian Prairie and its associations with 12 geographical, fragmentation and environmental attributes. Three AFLP primer pairs were used to screen 840 samples, representing approximately 30 samples from each population, and 246 polymorphic bands were scored. The fescue plant was genetically diverse, as revealed by the proportion of polymorphic bands (0.870–0.967), the mean band frequency (0.364–0.457) and the within‐population variation (69.4–85.4). The genetic variation was not highly differentiated, with only 6.5% of the total AFLP variation residing among populations. A Mantel test revealed a significant correlation between genetic and geographical distances ( r = 0.39; P < 0.004) and a spatial autocorrelation up to 60 km among populations was detected. The AFLP variation displayed a longitudinal decline and was significantly associated with environmental attributes related to moisture, indicating local adaptation. However, the AFLP variation was not significantly associated with the estimated population size and geographical distance to the nearest neighbor, suggesting that fragmentation has not generated considerable genetic impact on the fescue populations. Implications for fescue conservation, restoration and management are discussed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".