Geometric morphometric analyses (GMMs) of the leaf shape can distinguish two congeneric oak species Quercus dentata Thunberg and Quercus aliena Blume in sympatric areas.
Context High genetic and morphological variation in different Quercus species hinder efforts to distinguish them. In China, Q. dentata and Q. aliena are generally sympatrically distributed in warm temperate forests, and share some leaf morphological characteristics.
Aims The aim of this study was to use the morphometric methods to discriminate these sympatric Chinese oaks preliminarily identified from molecular markers.
Methods Three hundred sixty-seven trees of seven sympatric Q. dentata and Q. aliena populations were genetically assigned to one of the two species or hybrids using Bayesian clustering analysis based on nSSR. This grouping served as a priori classification of the trees. Shapes of 1835 leaves from the 367 trees were analyzed in terms of 13 characters (landmarks) by GMMs. Correlations between environmental and leaf morphology parameters were studied using linear regression analyses.
Results The two species were efficiently discriminated by the leaf morphology analyses (96.9 and 95.9% of sampled Q. aliena trees and Q. dentata trees were correctly identified), while putative hybrids between the two species were found to be morphologically intermediate. Moreover, we demonstrated that the leaf morphological variations of Q. aliena, Q. dentata, and their putative hybrids are correlated with environmental factors, possibly because the variation of leaf morphology is part of the response to different habitats and environmental disturbances.
Conclusion GMMs were able to correctly classify individuals from the two species preliminary identified as Q. dentata or Q. aliena by nSSR. The high degree of classification accuracy provided by this approach may be exploited to discriminate other problematic species and highlight its utility in plant ecology and evolution studies.
Geometric morphometrics, Genetic assignment, Leaf morphology, Quercus, Sympatric distribution
Liu, Y., Li, Y., Song, J. et al. Annals of Forest Science (2018) 75: 90.
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The datasets generated and/or analyzed during the current study are available from the authors on reasonable request.