Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves

Abstract

Premise: Leaf morphology is dynamic, continuously deforming during leaf expansion and among leaves within a shoot. Here, we measured the leaf morphology of more than 200 grapevines (Vitis spp.) over four years and modeled changes in leaf shape along the shoot to determine whether a composite leaf shape comprising all the leaves from a single shoot can better capture the variation and predict species identity compared with individual leaves.

Methods: Using homologous universal landmarks found in grapevine leaves, we modeled various morphological features as polynomial functions of leaf nodes. The resulting functions were used to reconstruct modeled leaf shapes across the shoots, generating composite leaves that comprehensively capture the spectrum of leaf morphologies present.

Results: We found that composite leaves are better predictors of species identity than individual leaves from the same plant. We were able to use composite leaves to predict the species identity of previously unassigned grapevines, which were verified with genotyping.

Discussion: Observations of individual leaf shape fail to capture the true diversity between species. Composite leaf shape-an assemblage of modeled leaf snapshots across the shoot-is a better representation of the dynamic and essential shapes of leaves, in addition to serving as a better predictor of species identity than individual leaves.

Keywords: Vitis; grapevine; landmark analysis; leaf shape; modeling; morphometrics.

Figure 1

Examples of changes in leaf traits between different developmental stages in different grapevine species. Vineyard‐collected leaves (adaxial side up, except for Vitis riparia) from the tip (top of stack) to base (bottom of stack) of the shoot. The size, shape, and color (among other traits) vary from node to node. Images are not to scale relative to each other.

Figure 2

Quantifying leaf shape changes along the shoot. (A) Two Vitis labrusca leaves from the second (left) and fourth (right) nodes from the shoot tip. Landmarks are indicated to the left, and lobe tips, sinuses, and associated nomenclature to the right. Leaves are scaled to show the allometric decrease in the ratio of vein‐to‐blade area that occurs during expansion. (B) Examples of morphological changes in V. riparia and V. labrusca leaves sampled along the shoot. Shoot tip, shoot base, nodes, and scale are indicated. Leaves in (A) are from the V. labrusca shoot shown here. (C) Diagrammatic representation of the methods used to quantify leaf shape change along the shoot. Leaves are first superimposed and scaled using Procrustean methods. The outlines shown were formed using Procrustean coordinates derived from the blade of each leaf shown in (B). The relative node position is calculated as the node number (starting at the tip) divided by the total leaf count (for the shoot), such that all nodes are assigned a fractional value between 0 and 1. Coordinate x‐ and y‐values are modeled as a function of relative node position. Dots connected between leaves by the dotted line correspond to landmark 19 of the distal sinus.

Figure 3

Modeling leaf shape along grapevine shoots with composite leaves. For each species, the modeled leaf shapes for 10 relative node positions along the shoot were superimposed and illustrated, forming a composite leaf. Illustrations are grouped by species‐relatedness: (A) Vitis riparia, V. acerifolia, and V. rupestris; (B) V. cinerea and V. vulpina; (C) V. aestivalis and V. labrusca; (D) V. coignetiae and V. amurensis; (E) V. palmata; (F) Ampelopsis glandulosa var. brevipedunculata. For each species, the number of leaves and vines sampled is given (note that every vine is sampled across four years, yielding a pseudoreplication of four). Composite leaves are colored as a gradient from gray (the shoot tip, node 1) to pink‐purple (the shoot base, node 10).

Figure 4

Principal component analysis (PCA) of individual vs. composite leaves. (A) Eigenleaf representations of shape variance at ±3 standard deviations explained by principal components (PCs) for a PCA performed on all individual leaves. The percent variance explained by each PC is shown on the left. (B, C) Comparison of results from two separate PCAs, one with all individual (B) and the other with composite (C) leaves. Confidence ellipses (95%) for four species (following the color legend) are provided in addition to all data points (gray). (D) Relative node position discretized into nodes counting from one to 10 projected onto the individual leaf PCA space. For the relative node position, there is no composite leaf PCA as the nodes are accounted for and integrated into the resulting values for that analysis. (E, F) Individual (E) and composite (F) leaf PCAs with 95% confidence ellipses for each year (following the color legend).

Figure 5

Comparison of linear discriminant analysis (LDA) results for individual vs. composite leaves. (A, B) Comparison of confusion matrices from two separate LDAs, one for individual (A) and the other for composite (B) leaves. The proportion of actual species (vertical) assigned to a predicted species identity (horizontal) is indicated by color. Vitis hybrids and species were not used in the training set and were only assigned an identity in the test set. (C) Confusion matrix for an LDA performed on the relative node position discretized into 10 nodes along the shoot for individual leaves. For the relative node position, there is no composite leaf LDA as the nodes are accounted for and integrated into the resulting values for that analysis. (D, E) Individual (D) and composite (E) leaf LDAs predicting year. All panels use the indicated color scheme for the assigned proportion, from 0 (white) to 1 (dark green).

Figure 6

Comparing species identity predictions based on morphology to known ancestry. (A) Ancestry for each individual using K = 10 from ADMIXTURE. Each population is assigned a different color. Species designations for each vine are as previously assigned for this collection, without prior genetic knowledge, and arranged by known phylogenetic relationships. Vines with genetic identities at odds with their assigned identity are indicated by black arrows and lowercase roman numerals. (B) For Vitis spp. with genetic information, the ancestry (left) and predicted species identity (based on morphology) for each shoot for each of the four study years (right) are provided. Morphological predictions consistent with genetic identity are indicated in bold. (C) Principal component analysis (PCA) of the same individuals in (A). Vines with conflicting assigned and genetic identities are indicated by black arrows and lowercase roman numerals as in (A). Vitis spp. in (B) are indicated by black dots and vine identification numbers. Species are indicated by colors that do not correspond with the color scheme of other panels, and the number of vines with genetic information is provided.