Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125, Modena, Italy; Centre for Forensic Anatomy and Biological Sciences, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.
Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125, Modena, Italy.
Zoology (Jena). 2020 Apr;139:125746. doi: 10.1016/j.zool.2020.125746. Epub 2020 Jan 24.
Quantitative analyses of morphological variation using geometric morphometrics are often performed on 2D photos of 3D structures. It is generally assumed that the error due to the flattening of the third dimension is negligible. However, despite hundreds of 2D studies, few have actually tested this assumption and none has done it on large animals, such as those typically classified as megafauna. We explore this issue in living equids, focusing on ventral cranial variation at both micro- and macro-evolutionary levels. By comparing 2D and 3D data, we found that size is well approximated, whereas shape is more strongly impacted by 2D inaccuracies, as it is especially evident in intra-specific analyses. The 2D approximation improves when shape differences are larger, as in macroevolution, but even at this level precise inter-individual similarity relationships are altered. Despite this, main patterns of sex, species and allometric variation in 2D were the same as in 3D, thus suggesting that 2D may be a source of 'noise' that does not mask the main signal in the data. However, the picture that emerges from this and other recent studies on 2D approximation of 3D structures is complex and any generalization premature. Morphometricians should therefore test the appropriateness of 2D using preliminary investigations in relation to the specific study questions in their own samples. We discuss whether this might be feasible using a reduced landmark configuration and smaller samples, which would save time and money. In an exploratory analysis, we found that in equids results seem robust to sampling, but become less precise and, with fewer landmarks, may slightly overestimate 2D inaccuracies.
使用几何形态测量学对三维结构的二维照片进行形态变异的定量分析是很常见的。通常认为，由于第三维的扁平化而产生的误差可以忽略不计。然而，尽管有数百项二维研究，但实际上很少有研究真正检验过这一假设，也没有研究在大型动物身上进行过这一假设，比如那些通常被归类为巨型动物的动物。我们在活体马科动物中探索了这个问题，重点研究了微观和宏观进化层面的颅下变异。通过比较二维和三维数据，我们发现大小可以很好地近似，而形状则受到二维不准确性的更大影响，这在种内分析中尤为明显。当形状差异较大时，二维近似度会提高，就像在宏观进化中一样，但即使在这个层面上，个体间的精确相似关系也会发生改变。尽管如此，二维的性别、物种和异速变异的主要模式与三维相同，因此表明二维可能是一种“噪声”源，不会掩盖数据中的主要信号。然而，从这项研究以及其他关于三维结构二维近似的最新研究中得出的结论是复杂的，任何概括都是不成熟的。因此，形态计量学家应该根据自己样本中特定的研究问题，通过初步调查来测试二维的适宜性。我们讨论了是否可以使用减少的地标配置和较小的样本量来实现这一点，这将节省时间和金钱。在探索性分析中，我们发现马科动物的结果似乎对采样具有鲁棒性，但变得不那么精确，而且地标数量较少，可能会略微高估二维不准确性。
