geomorph: an<scp>r</scp>package for the collection and analysis of geometric morphometric shape data

Abstract

Summary Many ecological and evolutionary studies seek to explain patterns of shape variation and its covariation with other variables. Geometric morphometrics is often used for this purpose, where a set of shape variables are obtained from landmark coordinates following a P rocrustes superimposition. We introduce geomorph: a software package for performing geometric morphometric shape analysis in the r statistical computing environment. Geomorph provides routines for all stages of landmark‐based geometric morphometric analyses in two and three‐dimensions. It is an open source package to read, manipulate, and digitize landmark data, generate shape variables via P rocrustes analysis for points, curves and surfaces, perform statistical analyses of shape variation and covariation, and to provide graphical depictions of shapes and patterns of shape variation. An important contribution of geomorph is the ability to perform P rocrustes superimposition on landmark points, as well as semilandmarks from curves and surfaces. A wide range of statistical methods germane to testing ecological and evolutionary hypotheses of shape variation are provided. These include standard multivariate methods such as principal components analysis, and approaches for multivariate regression and group comparison. Methods for more specialized analyses, such as for assessing shape allometry, comparing shape trajectories, examining morphological integration, and for assessing phylogenetic signal, are also included. Several functions are provided to graphically visualize results, including routines for examining variation in shape space, visualizing allometric trajectories, comparing specific shapes to one another and for plotting phylogenetic changes in morphospace. Finally, geomorph participates to make available advanced geometric morphometric analyses through the r statistical computing platform.

The authors' abstract, as published at the source. Methods in Ecology and Evolution, 2013 · DOI ↗