Compared to the large number of QTL studies for yield and other complex traits in wheat, there have been relatively few studies into individual grain morphometric components.
The first four morphometric principal components (PC) explain 86% of the overall variation and the first three meristic PCs explain 79% between historic walleye and "blue pike".
Geometric morphometric analysis, principal component analysis (PCA), canonical variates analysis (CVA), and other discriminant analyses (Goodall's F-test and Mahalanobis distance) were performed.
Morphometric analysis (Principal Components) was used to obtain these profiles of species and sex separations based on 77 cranial variables from 11 species of macaques.
To reduce the number of dimensions needed to describe the variation in the development of the external gills, we analyzed the data of the five morphometric measurements by principal component analysis (PCA), using the correlation coefficient matrix.
The classifications reveal that different landscape components or morphometric classes are important at different resolutions, and that morphometric classes exhibit resolution dependency in their geographical extents.
We performed our investigation by light and electron microscopy, as well as immunofluorescence, and we included morphometric quantification of tissue components and analysis of changes in the proliferation rates of interstitial cells.
Our morphometric data comprised mean principal component scores from a previous study of cranial shape variation across all extant ursid species [2].
Since the first principal component of the morphometric dataset proofed to be mainly influenced by the standard length, it was not used for analysis.
Morphological similarity indices (Fig. 5B), and a principal component analysis of morphometric and meristic measurements (Fig. 5C) further supported the phenotypically and genetically inferred identity of the parental species.
