Viacava Pietro, Blomberg Simone P, Weisbecker Vera
School of Biological Sciences The University of Queensland St Lucia QLD Australia.
College of Science and Engineering Flinders University Adelaide SA Australia.
Ecol Evol. 2023 Mar 28;13(3):e9698. doi: 10.1002/ece3.9698. eCollection 2023 Mar.
Morphology-based taxonomic research frequently applies linear morphometrics (LMM) in skulls to quantify species distinctions. The choice of which measurements to collect generally relies on the expertise of the investigators or a set of standard measurements, but this practice may ignore less obvious or common discriminatory characteristics. In addition, taxonomic analyses often ignore the potential for subgroups of an otherwise cohesive population to differ in shape purely due to size differences (or allometry). Geometric morphometrics (GMM) is more complicated as an acquisition technique but can offer a more holistic characterization of shape and provides a rigorous toolkit for accounting for allometry. In this study, we used linear discriminant analysis (LDA) to assess the discriminatory performance of four published LMM protocols and a 3D GMM dataset for three clades of antechinus known to differ subtly in shape. We assessed discrimination of raw data (which are frequently used by taxonomists); data with isometry (i.e., overall size) removed; and data after allometric correction (i.e., with nonuniform effects of size removed). When we visualized the principal component analysis (PCA) plots, we found that group discrimination among raw data was high for LMM. However, LMM datasets may inflate PC variance accounted in the first two PCs, relative to GMM. GMM discriminated groups better after isometry and allometry were removed in both PCA and LDA. Although LMM can be a powerful tool to discriminate taxonomic groups, we show that there is substantial risk that this discrimination comes from variation in size, rather than shape. This suggests that taxonomic measurement protocols might benefit from GMM-based pilot studies, because this offers the option of differentiating allometric and nonallometric shape differences between species, which can then inform on the development of the easier-to-apply LMM protocols.
基于形态学的分类学研究经常应用线性形态测量法（LMM）对头骨进行量化，以区分物种。通常，测量指标的选择依赖于研究者的专业知识或一套标准测量指标，但这种做法可能会忽略那些不太明显或常见的鉴别特征。此外，分类学分析往往忽视了这样一种可能性，即原本具有凝聚力的群体中的亚群可能纯粹由于大小差异（或异速生长）而在形状上有所不同。几何形态测量法（GMM）作为一种采集技术更为复杂，但它可以提供更全面的形状特征描述，并为解释异速生长提供一个严格的工具包。在本研究中，我们使用线性判别分析（LDA）来评估四种已发表的LMM方案和一个三维GMM数据集对已知在形状上有细微差异的三个袋鼬属进化枝的鉴别性能。我们评估了原始数据（分类学家经常使用的数据）、去除等比例（即总体大小）的数据以及经过异速生长校正（即去除大小的非均匀效应）的数据的鉴别能力。当我们可视化主成分分析（PCA）图时，我们发现LMM对原始数据的群体鉴别能力很强。然而，相对于GMM，LMM数据集在前两个主成分中所解释的主成分方差可能会被夸大。在PCA和LDA中去除等比例和异速生长因素后，GMM对群体的鉴别能力更强。尽管LMM可以成为区分分类群体的有力工具，但我们表明，这种区分很可能来自大小差异而非形状差异。这表明分类测量方案可能会从基于GMM的预试验研究中受益，因为这可以区分物种之间的异速生长和非异速生长形状差异，从而为更易于应用的LMM方案的制定提供参考。
