Phenotypic Variation of Oak Species ( <i>Quercus</i> spp.) Reveals Adaptive Strategies Across Natural and Semi‐Artificial Oak Stands
Bibliographic record
Abstract
ABSTRACT This study investigated leaf phenotypic variation in oak species to better understand how different groups of oaks adapt to diverse environmental conditions. We examined the leaf phenotypic traits of six oak populations in two mixed forests with differing species compositions: Zijin Mountain in Jiangsu Province, composed of Quercus acutissima , Q. variabilis , and Q. fabri ; and Youhua Village in Anhui Province, consisting of Q. acutissima , Q. chenii , and Q. fabri . The results indicated that species in the Cerris group ( Q. acutissima , Q. chenii , and Q. variabilis ) exhibited stable leaf morphology and higher fluctuating asymmetry (FA), suggesting adaptation to stable environments. In contrast, Q. fabri from the Quercus group showed higher phenotypic plasticity and lower FA, indicating a strategy for adapting to dynamic environments. The study also explored the relationship between FA and phenotypic plasticity, revealing that while both traits are influenced by environmental stress, phenotypic plasticity allowed for more flexible responses to environmental change. Additionally, our research highlighted the role of hybridization and genetic coadaptation in influencing developmental stability, with higher hybridization rates in Q. fabri leading to greater morphological variability. These findings underscore the importance of environmental factors, genetic variation, and hybridization in shaping the adaptive strategies and phenotypic traits of oak species, providing valuable insights into the complexities of adaptation and species identification.
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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 | 0.001 | 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) | 0.000 | 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".