Assessing the relationship between height growth and molecular genetic variation in Douglas-fir ( <i>Pseudotsuga menziesii</i> ) provenances
Bibliographic record
Abstract
Abstract Douglas-fir ( Pseudotsuga menziesii ) is a conifer tree native to western North America. In central Europe, it shows superior growth performance and is considered a suitable substitute for tree species impaired in vitality due to climate change. Maintenance and improvement of growth performance in a changing environment is a main challenge for forest tree breeders. In this context, genetic variation as a factor underlying phenotypic variation, but also as the basis for future adaptation, is of particular interest. The aims of this study were to analyse (i) genetic diversity of selected Douglas-fir provenances, (ii) variation in height growth among provenances, and (iii) to assess the link between genetic and phenotypic variation height growth. Genotyping was done on microsatellite loci. Effects of ‘provenance’, ‘genotype’, and ‘site’ on height growth were assessed by fitting mixed linear models. The most significant genetic differentiation was observed between provenances of the coastal variety, versus a provenance of the interior variety originating from British Columbia. Although genetic differentiation among provenances of the coastal variety was lower, genetic structures within this variety were identified. Moreover, genetic diversity showed a latitudinal gradient with the southernmost provenances being more diverse, probably reflecting the species' evolutionary history. The modelling approach revealed that height growth differed significantly by provenance, site, and the interaction between site and provenance, demonstrating that height growth is under strong genetic control. Additionally, this analysis showed that genetic variation captured by the genotyped microsatellite loci was significantly related to variation in height growth, providing statistical evidence for a genetic component in the observed phenotypic variation.
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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 | 0.001 | 0.001 |
| 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".