High-Density Morphometric Analysis of Shape and Integration: The Good, the Bad, and the Not-Really-a-Problem
High-Density Morphometric Analysis of Shape and Integration: The Good, the Bad, and the Not-Really-a-Problem
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DOI：
10.1093/icb/icz120
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发表时间：
2019-09-01
影响因子：
2.6
通讯作者：
Polly, P. David
中科院分区：
文献类型：
--
作者：
Goswami, Anjali;Watanabe, Akinobu;Polly, P. David
随着高分辨率三维（3D）数据的快速生成，比较形态学领域已经进入了一个新的阶段。有了数千个物种的免费3D数据，利用这种丰富的表型信息来量化形态的方法正在迅速出现。在这些技术中，高密度几何形态测量方法提供了一个强大的和通用的框架，以鲁棒地表征形状和表型整合，形态性状之间的协方差。这些方法对于分析复杂的结构和不同的分类群特别有用，这些分类群可能具有明确的同源性。然而，高密度的几何形态测量也带来了挑战，例如，与统计，但不是生物，协方差施加的放置和滑动的半地标和注册方法，如Procrustes叠加。在这里，我们提出了模拟和案例研究的高密度数据集的有鳞目动物，鸟类和盲肠动物的承诺和挑战的高维分析的表型整合和模块化。我们评估：（1）“大”高密度几何形态测量学数据相对于传统形状数据的相对优点;（2）Procrustes叠加对整合和模块化分析的影响;以及（3）使用高密度几何形态测量学和使用离散地标的分析之间整合模式的差异。我们证明，对于许多颅骨区域，需要20-30个地标和/或半地标，以准确地表征其形状变化，和地标只有分析做一个特别差的工作，捕捉拱顶和喙骨的形状变化。Procrustes叠加可以掩盖模块化，特别是当地标在平行方向上协变时，但这种效果随着生物学上更复杂的协变模式而降低。地标变化对质心位置的方向性影响比地标数量更影响协方差模式的恢复。在整体整合模式中，仅标志和标志加滑动半标志整合分析通常是一致的，但仅标志分析往往显示相邻骨之间的整合程度更高，尤其是当放置在骨之间缝线上的标志引入边界偏倚时。异速生长可能是一个更强的影响模式的集成在地标唯一的分析，这表明更强的整合之前，删除异速生长效应相比，分析包括半地标。高密度几何形态测量有其挑战和缺点，但我们对模拟和经验数据集的分析表明，这些潜在的问题不太可能掩盖真正的生物信号。相反，高密度的几何形态测量数据超过传统的地标为基础的方法在表征形态，并允许更细致入微的比较不同的类群。结合3D数据可用性的快速增长，高密度形态测量方法具有巨大的潜力，可以推动一类新的比较形态学和表型整合研究。
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 semi landmarks 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 semi landmarks 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-semi landmark 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 semi landmarks. 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.