Abstract
The field of comparative morphology has entered a new phase with the rapid generation of high-resolution three-dimensional (3D) data. With freely available 3D data of thousands of species, methods for quantifying morphology that harness this rich phenotypic information are quickly emerging. Among these techniques, high-density geometric morphometric approaches provide a powerful and versatile framework to robustly characterize shape and phenotypic integration, the covariances among morphological traits. These methods are particularly useful for analyses of complex structures and across disparate taxa, which may share few landmarks of unambiguous homology. However, high-density geometric morphometrics also brings challenges, for example, with statistical, but not biological, covariances imposed by placement and sliding of semilandmarks and registration methods such as Procrustes superimposition. Here, we present simulations and case studies of high-density datasets for squamates, birds, and caecilians that exemplify the promise and challenges of high-dimensional analyses of phenotypic integration and modularity. We assess: (1) the relative merits of “big” high-density geometric morphometrics data over traditional shape data; (2) the impact of Procrustes superimposition on analyses of integration and modularity; and (3) differences in patterns of integration between analyses using high-density geometric morphometrics and those using discrete landmarks. We demonstrate that for many skull regions, 20–30 landmarks and/or semilandmarks are needed to accurately characterize their shape variation, and landmark-only analyses do a particularly poor job of capturing shape variation in vault and rostrum bones. Procrustes superimposition can mask modularity, especially when landmarks covary in parallel directions, but this effect decreases with more biologically complex covariance patterns. The directional effect of landmark variation on the position of the centroid affects recovery of covariance patterns more than landmark number does. Landmark-only and landmark-plus-sliding-semilandmark analyses of integration are generally congruent in overall pattern of integration, but landmark-only analyses tend to show higher integration between adjacent bones, especially when landmarks placed on the sutures between bones introduces a boundary bias. Allometry may be a stronger influence on patterns of integration in landmark-only analyses, which show stronger integration prior to removal of allometric effects compared to analyses including semilandmarks. High-density geometric morphometrics has its challenges and drawbacks, but our analyses of simulated and empirical datasets demonstrate that these potential issues are unlikely to obscure genuine biological signal. Rather, high-density geometric morphometric data exceed traditional landmark-based methods in characterization of morphology and allow more nuanced comparisons across disparate taxa. Combined with the rapid increases in 3D data availability, high-density morphometric approaches have immense potential to propel a new class of studies of comparative morphology and phenotypic integration.
Highlights
Big data approaches to morphological studies have entered a new phase in recent years, due to the ubiquity of high-resolution imaging tools, such as microcomputed tomography imaging and surface scanning and photogrammetry (Davies et al 2017)
Discussions, and comparisons of these methods (Adams et al 2004, 2013; Bardua et al 2019a; Bookstein et al 2002; Boyer et al 2015; Gonzales et al 2016; Gunz and Mitteroecker 2013; Gunz et al 2005; Mitteroecker and Gunz 2009; Rohlf and Marcus 1993; Vitek et al 2017; Zelditch et al 2004) demonstrate the promise these methods offer for quantifying regions that are poorly characterized by use of only discrete landmarks, due to the lack of unambiguous homology across specimens or the presence of large areas without any appropriate structures at which to place landmarks
Capturing and quantifying morphology using high-resolution imaging has opened the door to high-density morphometric data analysis with semilandmarks or pseudolandmarks
Summary
Big data approaches to morphological studies have entered a new phase in recent years, due to the ubiquity of high-resolution imaging tools, such as microcomputed tomography imaging and surface scanning and photogrammetry (Davies et al 2017). This experiment suggests that the symmetry (or lack thereof) in the directions of covariance patterns within and between modules affects variability in position of the centroid from one shape to the and that the degree of variation in the position of the centroid relative to variation in individual landmarks is a major determinant of how much Procrustes superimposition, which recenters shapes on their centroids, alters the covariance structure.
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