Data from: Non-landmark classification in paleobiology: computational geometry as a tool for species discrimination

One important and sometimes contentious challenge in paleobiology is discriminating between species, which is increasingly accomplished by comparing specimen shape. While lengths and proportions are needed to achieve this task, finer geometric information, such as concavity, convexity, and curvature, plays a crucial role in the undertaking. Nonetheless, standard morphometric methodologies such as landmark analysis are not able to capture in a quantitative way these features and other important fine-scale geometric notions. Here we develop and implement state-of-the-art techniques from the emerging field of computational geometry to tackle this problem with the Mississippian blastoid Pentremites. We adapt previously known computational framework to produce a measure of dissimilarity between shapes. More precisely, we compute “distances” between pairs of 3D surface scans of specimens by comparing a mix of global and fine-scale geometric measurements. This process uses the 3D scan of a specimen as a whole piece of data incorporating complete geometric information about the shape; as a result, scans used must accurately reflect the geometry of whole, undamaged, undeformed specimens. Using this information we are able to represent these data in clusters, and ultimately reproduce and refine results obtained in previous work on species discrimination. Our methodology is landmark-free, and therefore faster and less prone to human error than previous landmark-based methodologies.

古生物学领域一项重要且时常引发争议的挑战，便是物种鉴别，而如今这一任务愈发依赖对标本形态的比对分析。尽管完成该任务需要考量长度与比例，但诸如凹度、凸度、曲率这类更精细的几何信息，对这项工作而言至关重要。然而，诸如标志点分析（landmark analysis）这类主流形态测量方法，无法以定量方式捕捉上述特征及其他重要的精细尺度几何概念。为此，我们针对密西西比纪海蕾类五孔海蕾（Pentremites）这一研究对象，开发并应用了来自新兴计算几何学领域的前沿技术，以解决上述物种鉴别的难题。我们改良了已有的计算框架，以构建形态间相异度的量化指标。更具体而言，我们通过融合全局与精细尺度几何测量指标，对成对的标本三维表面扫描数据计算“距离”值。该流程将单份标本的三维扫描数据作为整体数据集，纳入该标本形态的全部几何信息；因此，所使用的扫描数据必须精准反映完整、未受损且未发生形变的标本几何特征。借助此类信息，我们可将数据进行聚类，并最终复现并优化此前物种鉴别相关研究中所得出的结果。我们的方法无需使用标志点，因此相较于此前基于标志点的分析方法，运算速度更快，且受人为误差的影响更小。