Background

There are a variety of approaches to digital leaf shape morphometrics, including outline or Fourier analysis, contour signatures, landmark analysis, shape features, fractal dimensions, and texture analysis (Cope et al., 2012). Among these analyses, landmark and Fourier analysis together perform exceptionally well at distinguishing between groups among leaf shapes (McLellan and Endler, 1998; Hearn, 2009). Landmark analysis is ideal for capturing aspects of shape that are consistent among all leaves within a given dataset. The selection of landmarks should include points that are biologically homologous and adequately represent the morphology of the leaf (see more pointers in Bookstein, 1991 and Zelditch et al., 2004). If leaves in the dataset do not have evolutionarily conserved shape features, ‘pseudo-landmarks’ can instead be placed (Chitwood and Sinha, 2016); that is, landmarks can be placed at equidistant points along the leaf outline relative to homologous points that act as anchors. Landmarks can then be analyzed using Generalized Procrustes Analysis (GPA), which normalizes shape data (annotated by landmarks) at equal scale, allowing for an accurate comparison of shapes regardless of their size. Outline analysis offers a more broadly applicable phenotyping method in that Elliptical Fourier Descriptors (EFDs) are used to build shape descriptors of the leaf outline (Kuhl and Giardina, 1982; Iwata and Ukai, 2002). While sensitive to noise, EFDs are ideal for large leaf datasets that have subtle differences between shapes. Shape features are an additional, simple method of outline analysis that can include the perimeter to area ratio, aspect ratio, and circularity measurements (Cope et al., 2012). In this protocol, we focus on aspect ratio and circularity, as they detect signatures of lobing and serration. Aspect ratio is the ratio of the major axis to the minor axis of a fitted ellipse, in which case values close to ‘1’ are more circular in shape regardless of lobing. Circularity is the ratio of the leaf area to perimeter outline. This measurement is useful for discriminating leaves with lobing and serration, with low circularity values indicating significant lobing and serration. This protocol is designed such that researchers can choose between all three methods (GPA, EFD, and shape features) based on which analyses best fit their data.