ABSTRACT
The quantification of floral shape variations is difficult because flower structures are both diverse and complex. Traditionally, floral shape variations are quantified using the qualitative and linear measurements of two-dimensional (2D) images. The 2D images cannot adequately describe flower structures, and thus lead to unsatisfactory discrimination of the flower shape. This study aimed to acquire three-dimensional (3D) images by using microcomputed tomography (µCT) and to examine the floral shape variations by using geometric morphometrics (GM). To demonstrate the advantages of the 3D-µCT-GM approach, we applied the approach to a second-generation population of florist's gloxinia (Sinningia speciosa) crossed from parents of zygomorphic and actinomorphic flowers. The flowers in the population considerably vary in size and shape, thereby served as good materials to test the applicability of the proposed phenotyping approach. Procedures were developed to acquire 3D volumetric flower images using a µCT scanner, to segment the flower regions from the background, and to select homologous characteristic points (i.e., landmarks) from the flower images for the subsequent GM analysis. The procedures identified 95 landmarks for each flower and thus improved the capability of describing and illustrating the flower shapes, compared with typically lower number of landmarks in 2D analyses. The GM analysis demonstrated that flower opening and dorsoventral symmetry were the principal shape variations of the flowers. The degrees of flower opening and corolla asymmetry were then subsequently quantified directly from the 3D flower images. The 3D-µCT-GM approach revealed shape variations that could not be identified using typical 2D approaches and accurately quantified the flower traits that presented a challenge in 2D images. The approach opens new avenues to investigate floral shape variations.