Genetic Variation and the Relationships Among Growth, Morphological, and Physiological Traits in Pterocarpus macrocarpus: Implications for Early Selection and Conservation
Bibliographic record
Abstract
Understanding genetic variation in commercially valuable tree species is essential for improving breeding and conservation efforts. This study investigates genetic variation, heritability, and trait relationships in Pterocarpus macrocarpus, a vital hardwood species for Thailand’s reforestation initiatives. We evaluated growth (height and diameter), morphology (biomass dry weight and specific leaf weight), and physiological traits (net photosynthesis [A], transpiration rate [E], and water-use efficiency [WUE]) across 112 open-pollinated families from six natural populations under controlled nursery conditions over 30 weeks. Using a randomised complete block design, variance and covariance analyses were conducted to estimate genetic parameters. Seedling survival reached 95%, confirming favourable conditions for genetic expression. There were significant differences among populations and families within populations in growth and biomass. In contrast, physiological traits showed notable family-level variation (A, E, WUE) and only population effects for WUE. Residual variance was predominant across traits, indicating considerable within-family variation. Growth and biomass exhibited moderate to high heritability (individual: 0.39–1.00; family: 0.61–0.90), while specific leaf weight and shoot-to-root ratio had lower heritability at the individual level. Physiological traits showed low to moderate heritabilities (individual: 0.26–0.43; family: 0.47–0.62), with maternal effects via seed weight significantly influencing early growth. The heritability of height decreased over time, whereas the heritability of diameter remained stable. Strong genetic correlations among growth and biomass suggest the potential for combined selection gains. However, physiological traits show weak or no correlations with growth, highlighting their independent genetic control. Variation at the population level in growth and WUE may reflect adaptive responses to seed-source environments. Our findings support the use of nursery-based screening as a cost-effective method for the early identification of high-quality families. WUE is a promising focus for breeding programs targeting drought-prone regions. This study provides key insights for advancing the genetic improvement and conservation of P. macrocarpus, emphasizing the importance of incorporating physiological traits into breeding and conservation strategies.
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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 | 0.000 | 0.000 |
| 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".