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  5. Assessing Leaf Morphometric Symmetry of Four Chinese Quercus Species in a Mixed Forest

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Article
en
2022

Assessing Leaf Morphometric Symmetry of Four Chinese Quercus Species in a Mixed Forest

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en
2022
Vol 13 (10)
Vol. 13
DOI: 10.3390/f13101635

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Yanming Fang
Yanming Fang

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Xuan Li
Xiaojing Yu
Jiefan Huang
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Abstract

(1) Background: Oaks have achieved notoriety for sufficient levels of sympatric species richness allowing hybridization, thus generating substantial phenotypic variation. Leaf fluctuation asymmetry is an important attribute, as it reflects not only genetic variability but also species buffering capacity. (2) Methods: We investigated the phenotypic diversity of four-oak species (Quercus acutissima, Q. variabilis, Q. fabri, and Q. serrata var. brevipetiolata) using leaf geometric morphometric analysis. Eight leaf morphological indicators (length, width, perimeter, area, left and right areas, areal ratio, and normalized symmetry index) were used to determine the hybridization level, whereas bilateral symmetry indicators were used to assess species environmental adaptation; (3) Results: Phenotypic variation ranged from 1.54 to 29.35 folds and significantly diverged among the studied species. Taxonomically species in Section Quercus (Q. fabri and Q. serrata var. brevipetiolata) are lower than those in Section Cerris (Q. acutissima and Q. variabilis) with good bilateral symmetry. The bilateral symmetry index of Q. variabilis had a larger range of variation, indicating better environmental adaptability; (4) Conclusions: We presume that species in Section Quercus with less leaf fluctuation asymmetry have a high level of genetic heterozygosity; however, this assumption requires further verification. The observed phenotypic diversity reflects a combination of environmental and genetic factors.

How to cite this publication

Xuan Li, Xiaojing Yu, Jiefan Huang, Yousry A. EI-Kassaby, Yanming Fang (2022). Assessing Leaf Morphometric Symmetry of Four Chinese Quercus Species in a Mixed Forest. , 13(10), DOI: https://doi.org/10.3390/f13101635.

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Publication Details

Type

Article

Year

2022

Authors

5

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.3390/f13101635

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