Guangxi Zhiwu (Sep 2023)

Phenotypic diversity of Quercus gilva natural populations in middle subtropical China

  • Zhikuang QIN,
  • Na LIU,
  • Xia ZHOU,
  • Zhihui LI,
  • Fuliang CAO,
  • He LI

DOI
https://doi.org/10.11931/guihaia.gxzw202205046
Journal volume & issue
Vol. 43, no. 9
pp. 1622 – 1635

Abstract

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Quercus gilva is a precious timber tree species in middle subtropical China. In order to investigate the phenotypic diversity, phenotypic variation pattern and the major geographic and climatic factors influencing phenotypic variation of Q. gilva populations, 15 growth and leaf traits of 115 individuals from 14 natural populations were measured, and then the phenotypic diversity, variation pattern and the correlation between phenotypic variation and geographic and climatic factors were studied using nested analysis of variance, phenotypic differentiation coefficient, diversity index, correlation analysis, principal component analysis and cluster analysis. The results were as follows: (1) The average variation coefficient and the average Shannon-Wiener index of 15 phenotypic traits were 35.070% and 1.998, respectively. Variation coefficient and Shannon-Wiener index of 14 populations ranged from 14.94% (Dongkou) to 35.56% (Longshan) and from 1.127 (Songyang) to 1.980 (Changning), respectively. These results indicated that Q. gilva showed a relatively high level of phenotypic diversity. (2) Significant differences in 15 phenotypic traits were found among and within populations (P<0.01), with an average phenotypic differentiation coefficient of 41.491%. This result elucidated that the phenotypic variation within populations was greater than that among populations. (3) Correlation analysis showed that there were significant or entremely significant correlations among parts of the traits, whereas no correlation was found between phenotypic traits and geographic factors. Precipitation was the major factor that influenced the phenotypic traits of Q. gilva. (4) Principle component analysis indicated that the first four principal components explained 82.961% of total phenotypic contribution rate of Q. gilva. (5) Cluster analysis revealed that 14 Q. gilva populations were divided into three groups according to their different leaf and petiole size, such as small-leaved micropetiole, large-leaved macropetiole and medium-leaved micropetiole, which was not related to the geographic distribution of populations. This study provides scientific basis for the protection and utilization of Q. gilva germplasm resources, and lays an important foundation for its improved variety breeding.

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