Landmark Typology in Applied Morphometrics Studies: What's the Point? (original) (raw)
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2019: Landmark Typology in Applied Morphometrics Studies: What's the Point
The Anatomical Record, 2019
Landmarks are the hallmark of biological shape analysis as discrete anatomical points of correspondence. Various systems have been developed for their classification. In the most widely used system, developed by Bookstein in the 1990s, landmarks are divided into three distinct types based on their anatomical locations and biological significance. As Bookstein and others have argued that different landmark types possess different qualities, e.g., that Type 3 landmarks contain deficient information about shape variation and are less reliably measured, researchers began using landmark types as justification for selecting or avoiding particular landmarks for measurement or analysis. Here, we demonstrate considerable variation in landmark classifications among 17 studies using geometric morphometrics (GM), due to disagreement in the application of both Bookstein's landmark typology and individual landmark definitions. A review of the literature furthermore shows little correlation between landmark type and measurement reproducibility, especially when factors such as differences in measurement tools (calipers, digitizer, or computer software) and data sources (dry crania, 3D models, or 2D images) are considered. Although landmark typology is valuable when teaching biological shape analysis, we find that employing it in research design introduces confusion without providing useful information. Instead, researchers should choose landmark configurations based on their ability to test specific research hypotheses, and research papers should include justifications of landmark choices along with landmark definitions, details on landmark collection methods, and appropriate interobserver and intraobserver analyses. Hence, while the landmarks themselves are crucial for GM, we argue that their typology is of little use in applied studies.
Geometric Morphometrics: Ten Years
2002
The analysis of shape is a fundamental part of much biological research. As the field of statistics developed, so have the sophistication of the analysis of these types of data. This lead to multivariate morphometrics in which suites of measurements were analyzed together using Canonical Variates analysis, Principal Components Analysis, and related methods. In the 1980s, a fundamental change began in the nature of the data gathered and analyzed. This change focused on the coordinates of landmarks and the geometric information about their relative positions. As a by-product of such an approach, results of multivariate analyses could be visualized as configurations of landmarks back in the original space of the organism rather than only as statistical scatter plots. This new approach, called “geometric morphometrics,” had benefits that lead Rohlf and Marcus (1993) to proclaim a “revolution” in morphometrics. In this paper, we briefly update the discussion in that paper and summarize t...
Integrative and Comparative Biology, 2019
Synopsis 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 superimpo-sition 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.