Spatial genetic structure of lowbush blueberry,<i>Vaccinium angustifolium</i>, in four fields in Maine
Bibliographic record
Abstract
Expressed sequence tag – polymerase chain reaction (EST-PCR) molecular markers were used to infer spatial genetic structure of four lowbush blueberry ( Vaccinium angustifolium Ait.) fields in Maine. Genetic structure was quantified at three spatial scales: (1) within apparent clones (intrapatch), (2) among clones within a field, and (3) among fields separated by as much as 65 km. Of five “clones” or putative individuals examined in the intrapatch study, two showed complete genetic homogeneity within the patch, while three showed some band differences at their edges compared with their interiors. These differences at the edges, however, matched adjacent clones (so-called “intruders”), from which it was concluded that lowbush blueberry exhibits a fairly tight, phalanx clonal architecture with no evidence of invasive seedling establishment within clones. No significant correlation between genetic and physical distance was found among clones within fields via several statistical approaches. Significant among-field genetic differentiation was found via AMOVA (ΦPT= 8.4%; p ≤ 0.01) based upon transect samples across four fields ranging from 12.5 to 65 km apart. Principal component analysis and spatial autocorrelation (SA) corroborated these findings. Significant positive SA was found at the within-field distance class of <350 m, but SA decreased to an insignificant value by the first interfield distance of 12.5 km. A special form of SA analysis was employed to detect “hotspots” of genetic similarity between pairs of adjacent clones in two fields. Results indicated that 5 of 23 pairs of clones (21.7%) were genetically similar to each other, while the majority of pairs (18 of 23; 78.3%) showed random, decreasing patterns of genetic similarity. Results are discussed in terms of clonal dynamics including architecture, seedling recruitment, and inferred pollen or seed dispersal distances.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".