Spatial genetic structure in populations of<i>Quercus mongolica</i>var.<i>grosseserrata</i>(Fagaceae) from southern Korea
Bibliographic record
Abstract
Multilocus, putative allozyme genotypes were mapped and sampled from two local populations of Quercus mongolica Fischer ex Turcz var. grosseserrata (Bl.) Rehder & Wilson (Fagaceae) (each area is 100 m × 100 m, one with Sasa cover (N = 62) versus a second without it (N = 384)) occurring in undisturbed forests near Nogodan, Mount Jiri in southern Korea. Ripley's L-statistics and spatial autocorrelation analysis (a coancestry coefficient, fij) were used to test the prediction that because of low seedling establishment in a population with dense Sasa cover, there would be no spatial aggregation or hyperdispersion of individual trees and little evidence of fine-scale genetic structure in the population. As predicted, the Sasa-covered population showed no evidence of significant aggregation of individuals (P < 0.01) up to an interplant distance of 50 m and a random distribution of putative genotypes in the population. By contrast, the L-statistics conducted in the Sasa-free population indicated significant aggregation of individuals at interplant distances extending from 4 to 50 m. Spatial autocorrelation analysis revealed small but significant (P < 0.01), positive, fine-scale genetic structure extending from 10 to 30 m. A very similar result was obtained from 100 replicates each consisting of 62 trees in the Sasa-free populations by applying rarefaction and bootstrapping. These findings support the hypothesis that ground vegetation such as Sasa spp. has an impact on fine-scale genetic structure. The weak spatial genetic structure found in the Sasa-free population may primarily be due to limited acorn dispersal coupled with overlapping seed shadows and (or) secondary acorn dispersal by rodents.Key words: allozymes, Fagaceae, ground cover, Quercus mongolica var. grosseserrata, Sasa spp., spatial genetic structure.
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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) | 0.000 | 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".