Computational protocol: Digital Morphometrics of Two North American Grapevines (Vitis: Vitaceae) Quantifies Leaf Variation between Species, within Species, and among Individuals

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[…] To identify leaf shape variation, we utilized generalized Procrustes analysis (GPA), a method of shape comparison that scales the data equally to eliminate the effects of different-sized objects, resulting in an analysis that examines differences among shapes only (). GPA is applied to landmark data that represent homologous points of shape, in this case important grapevine leaf features. Following , ) we applied 17 landmark points on each leaf to capture details of the leaf outline such as lobes and sinuses (12 “outer landmarks”) and vein architecture (5 “inner landmarks”) (Figure ). Landmarks were placed on leaf images using the software package ImageJ (). Following landmark dataset assembly, GPA was implemented in R () using the ‘procGPA’ function in the “shapes” package (), generating 34 principal component (PC) scores for each leaf and percent variance explained by each PC. Eigenleaves were visualized using the ‘shapepca function.’ Visualization of average shape outlines extracted from Procrustes coordinates for each genotype were plotted using custom R scripts and in the R package ggplot2 (). All code is available on GitHub.In order to further investigate differences in leaf shape within genotypes among clones, among genotypes, and among species, we performed linear discriminants analysis (LDA) on the landmark data using R. LDA is a statistical classification method consisting of mechanized pattern detection that can be used to distinguish two or more classes of objects in a dataset (e.g., species, genotypes, or disease). Linear discriminants were determined using the ‘lda’ function in the R package MASS (). Those linear discriminants, which are multivariate classifications similar to PCs, are then used to classify the leaves in the data set, blind to their assigned identity, according to class (i.e., species, genotype, or disease) using the ‘predict’ function. The end result is visualized as a table of predicted vs. actual class (i.e., species, genotype, disease) identity.A second approach using elliptical Fourier descriptors (EFDs) was employed to look at differences in overall leaf shape within and among genotypes and species. Individual scanned leaves were converted to binary images (i.e., black leaf image on a white background) using custom macros in ImageJ for chain coding. Occasionally, some leaves were damaged or diseased, resulting in deformed leaf shapes (see below); these leaves were removed from the EFD dataset. Each binary image was converted into chain code using the program SHAPE v1.3 (). EFD analysis begins by building chain code along the perimeter of each leaf to create a harmonic series (). Chain code contours were converted to normalized EFDs for Fourier analysis. In the R package Momocs (), function ‘nef2coe’ was used to convert normalized EFDs to harmonic coefficients, or ‘coe’ objects. The ‘coe’ objects were analyzed for differences in leaf shape outline using PCA and visualized using the ‘dudi.plot’ function. For each genotype, an average outline shape was calculated using the ‘meanShapes’ function, then visualized using function ‘tps.iso’ in the Momocs R package. […]

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