Bioinformatic and biometric methods in plant morphology1

Digital morphometrics: Application of MorphoLeaf in shape visualization and species delimitation, using Cucurbitaceae leaves as a model

PREMISE

Plant leaves are one of the most important organs for plant identification due to their variability across different taxonomic groups. While traditional morphometrics has contributed tremendously to reducing the problems accompanying plant identification and morphology-based species delimitation, image-analysis digital solutions have made it easy to detect more characters to complement existing leaf data sets.

METHODS

Here, we apply MorphoLeaf to generate a morphometric data set from 140 leaf specimens of seven Cucurbitaceae species via landmark extraction, the reparameterization of leaf contours, and data quantification and normalization. A statistical analysis was performed on the resulting data set.

RESULTS

A principal component analysis revealed that leaf blade area, blade perimeter, tooth area, tooth perimeter, the measure of the distance from tooth position to the tip, and the measure of the distance from tooth position to the base are important and informative landmarks that contribute to the variation within the species studied.

DISCUSSION

MorphoLeaf can be applied to quantitatively track leaf diversity, thereby functionally integrating morphometrics and shape visualization into the digital identification of plants. The success of digital morphometrics in leaf outline analyses presents researchers with opportunities to carry out more accurate image-based research in areas such as plant development, evolution, and phenotyping.

Biometrics Assessment of Cluster- and Berry-Related Traits of Muscadine Grape Population

In this study, biometrics assessment of flower structure, cluster-, and berry-related traits were evaluated in a population of 90 muscadine grape genotypes for three consecutive years. This population consisted of 21 standard cultivars, 60 breeding lines, and 9 Vitis x Muscadinia hybrids (VM hybrids). Cluster length (CL) and width (CWI) characteristics exhibited slight differences among the population, with a range estimated at 7.1 and 4.6 cm, respectively. However, cluster weight (CWE), number of berries/cluster (N.B/C), and cluster compactness (CC) traits showed more diversity between individuals with a calculated range of 205.6 g, 32.6 B/C, and 24.1, respectively. Interestingly, all berry-related traits greatly varied between individuals, excluding the number of seeds/berry (N.S/B) character. The N.S/B trait displayed a narrow range of 5.6 seeds within the population. However, characters of berry length (BL), width (BWI), weight (BWE), the weight of seeds/berry (W.S/B), firmness (FF), and dry scar pattern (SP) demonstrated a wide estimated range of 21.2 mm, 21.7 mm, 25.4 g, 0.71 g, 0.21 N, and 82%, respectively. Normal distribution analysis for each trait suggested different distribution patterns extended between unimodal to multimodal behavior. Hierarchical mapping analysis was able to classify the population into several clades based on physical cluster- and berry-related attributes. The PCA suggested that hermaphroditic (perfect) flower structure is associated with compact clusters exhibiting small berries in size and weight (i.e., muscadine genotypes suitable for wine production). However, female flower structure is associated with clusters displaying large berries in size and weight (i.e., muscadine genotypes appropriate for fresh consumption). These patterns occurred independently of cluster size and weight characters. This research is the first study evaluating muscadine biometrics characters at a population level, providing valuable information for market demand and muscadine breeding programs.

Arabidopsis phenotyping through geometric morphometrics

Background

Recently, great technical progress has been achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it possible to extract shape and size parameters for genetic, physiological, and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of the platform and segmentation software used are still lacking, and shape descriptions still rely on ad hoc or even contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis, and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations among groups and measure them in shape distance units.

Results

Here, a particular scheme of landmark placement on Arabidopsis rosette images is proposed to study shape variation in viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown, and reproducibility issues are assessed.

Conclusions

Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.

Biometric approach in selecting plants for phytoaccumulation of uranium

This paper promotes the biometric classification system of plant cultivars, unique characteristics, in terms of the uranium (U) uptake, primarily in the function of the application for phytoremediation. It is known that the degree of adoption of U depends on the plant species and its morphological and physiological properties, but it is less known what impact have plants cultivars, sorts, and hybrids. Therefore, we investigated the U adoption in four cultivars of three plant species (corn, sunflower and soy bean). "Vegetation experiments were carried out in a plastic-house filled with soil (0.66 mgU) and with tailing (15.3 mgU kg(-1)) from closed uranium mine Gabrovnica-Kalna southeast of Serbia". Principal Component Analysis (PCA), Cluster Analysis (CA) and analysis of variance (ANOVA) were used for assessing the effect of different substrates cultivars, plant species and plant organs (root or shoot) on U uptake. Obtained results showed that a difference in U uptake by three investigated plant species depends not only of the type of substrate types and plant organs but also of their cultivars. Biometrics techniques provide a good opportunity for a better understanding the behavior of plants and obtaining much more useful information from the original data.