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 vital tree species is critical for optimizing breeding and conservation strategies. This study evaluates genetic variation, heritability, and trait correlations in Pterocarpus macrocarpus, a high-value hardwood central to Thailand’s reforestation efforts. We assessed growth (height, diameter), morphological (biomass dry weight, specific leaf weight), and physiological traits (net photosynthesis [A], transpiration rate [E], water-use efficiency [WUE]) across 112 open-pollinated families from six natural populations under controlled nursery conditions over 30 weeks. Using a randomized complete block design, variance and covariance analyses were conducted to estimate genetic parameters. Seedling survival reached 95%, confirming favorable conditions for genetic expression. Populations and families within populations exhibited significant divergence in growth and biomass traits, while physiological traits showed pronounced family-level variation (A, E, WUE) and population effects for WUE only. Residual variance dominated across traits, highlighting substantial within-family variation. Growth and biomass traits demonstrated moderate to high heritability (individual: 0.386–1.000; family: 0.614–0.895), whereas specific leaf weight and shoot-to-root ratio displayed lower individual heritability. Physiological traits exhibited low to moderate heritability (individual: 0.256–0.428; family: 0.473–0.615), with maternal effects (via seed weight) significantly influencing early growth. Height heritability declined over time, contrasting with stable diameter heritability. Strong genetic correlations among growth and biomass traits suggest potential for simultaneous selection gains. Physiological traits, however, exhibit weak or no correlation with growth, highlighting their independent genetic regulation. Population-level variation in growth and WUE may illustrate adaptive responses to seed-source environments. Our findings support nursery-based screening as a cost-effective tool for the early selection of superior families. WUE serves as a promising target for breeding programs focused on drought-prone sites. This study provides foundational insights for advancing P. macrocarpus genetic improvement and conservation, emphasizing the integration of physiological traits into breeding and conservation strategies.
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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 | 0.001 | 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 | 0.001 | 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 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".