SSR Allelic Diversity Shifts in Zoysiagrass (<i>Zoysia</i> spp.) Cultivars Released from 1910 to 2016
Bibliographic record
Abstract
Selection during varietal improvement has been shown to reduce genetic diversity in several different crop species. A reduction in genetic diversity can be detrimental to future breeding efforts and increase susceptibility to biotic stresses. The purpose of this study was to analyze changes in levels of allelic diversity at the gene and population levels in 40 zoysiagrass (Zoysia spp. Willd.) cultivars released between 1910 and 2016 using simple sequence repeat (SSR) markers. Fifty‐six SSR primer pairs were used to determine whether allelic diversity has changed among cultivars released over a century of modern plant breeding. While no significant differences were observed for total numbers of alleles or genetic similarity values between the initial and most recent time periods, our results demonstrate that genetic diversity among zoysiagrass cultivars has fluctuated over time. Allelic diversity declined during the 1990s and then recovered during 2000s. STRUCTURE analysis revealed five subpopulations with varying levels of admixture and that, for the most part, good representation of these subpopulations has been maintained over the different decades of release. These findings illustrate that plant breeding has not resulted in a substantial decline in zoysiagrass genetic diversity, but also that the potential exists for increasing diversity through future germplasm collections and the inclusion of less‐used Zoysia species to create new combinations of alleles.
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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 | 0.000 | 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) | 0.001 | 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".