Search for: All records

Abstract Geometric morphometric (GM) tools are essential for meaningfully quantifying and understanding patterns of variation in complex traits like shape. In this field, the breadth of answerable questions has grown dramatically in recent years through the development of new analyses and increased computational efficiency.In this note, we describe the ways in whichgeomorph, a widely usedRpackage for quantifying and analysing GM data, has grown with the field.We presentgeomorph v4.0and describe the ways in which this version has dramatically improved upon previous versions. We also present a new graphical user interface for easy implementation,gmShiny.These contributions positiongeomorphto be the primary tool for GM analyses, particularly those employing a phylogenetic comparative approach. 

Abstract It has become common in evolutionary biology to characterize phenotypes multivariately. However, visualizing macroevolutionary trends in multivariate datasets requires appropriate ordination methods.In this paper we describe phylogenetically aligned component analysis (PACA): a new ordination approach that aligns phenotypic data with phylogenetic signal. Unlike phylogenetic principal component analysis (Phy‐PCA), which finds an alignment of a principal eigenvector that is independent of phylogenetic signal, PACA maximizes variation in directions that describe phylogenetic signal, while simultaneously preserving the Euclidean distances among observations in the data space.We demonstrate with simulated and empirical examples that with PACA, it is possible to visualize the trend in phylogenetic signal in multivariate data spaces, irrespective of other signals in the data. In conjunction with Phy‐PCA, one can visualize both phylogenetic signal and trends in data independent of phylogenetic signal.Phylogenetically aligned component analysis can distinguish between weak phylogenetic signals and strong signals concentrated in only a portion of all data dimensions. We provide empirical examples that emphasize the difference. Use of PACA in studies focused on phylogenetic signal should enable much more precise description of the phylogenetic signal, as a result.Overall, PACA will return a projection that shows the most phylogenetic signal in the first few components, irrespective of other signals in the data. By comparing Phy‐PCA and PACA results, one may glean the relative importance of phylogenetic and other (ecological) signals in the data.