Whole-tree silvic identifications and the microsatellite genetic structure of a red oak species complex in an Indiana old-growth forest
Bibliographic record
Abstract
The red oaks (Quercus section Lobatae) include important timber species, but we know little about their gene pools. Red oak species can be difficult to identify, possibly because of extensive interspecific hybridization, although most evidence of this is morphological. We used 15 microsatellite loci to examine the genetic composition of a red oak community in 20.6 ha of an Indiana old-growth forest. The community included northern red oak (Quercus rubra L.), Shumard oak (Quercus shumardii Buckl.), and pin oak (Quercus palustris Muenchh.). Species were identified using whole-tree silvic characters, the approach most often implemented by foresters. We found high genetic diversity within species but limited genetic differences between species. Phenetic clustering showed that Q. rubra and Q. shumardii were more genetically similar than either was to Q. palustris, but a neighbor-joining tree revealed that individuals of the different species did not resolve into single-species clusters. We identified four mixed-species subpopulations using Structure, a computer program based on Monte Carlo simulation. The three largest groups are consistent with the following biological interpretations: (i) pure Q. rubra, (ii) Q. rubra, Q. shumardii, and their hybrids, and (iii) Q. rubra, Q. shumardii, Q. palustris, and their hybrids. We discuss the implications of these findings for the whole-tree silvic approach to selection and for management of the red oak gene pool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".