Plant light interception can be explained via computed tomography scanning: demonstration with pyramidal cedar (Thuja occidentalis, Fastigiata)

Learning how a tree branches out: A statistical modeling approach

The increasingly large size of the graphical and numerical data sets collected with modern technologies requires constant update and upgrade of the statistical models, methods and procedures to be used for their analysis in order to optimize learning and maximize knowledge and understanding. This is the case for plant CT scanning (CT: computed tomography), including applications aimed at studying leaf canopies and the structural complexity of the branching patterns that support them in trees. Therefore, we first show after a brief review, how the CT scanning data can be leveraged by constructing an analytical representation of a tree branching structure where each branch is represented by a line segment in 3D and classified in a level of a hierarchy, starting with the trunk (level 1). Each segment, or branch, is characterized by four variables: (i) the position on its parent, (ii) its orientation, a unit vector in 3D, (iii) its length, and (iv) the number of offspring that it bears. The branching structure of a tree can then be investigated by calculating descriptive statistics on these four variables. A deeper analysis, based on statistical models aiming to explain how the characteristics of a branch are associated with those of its parents, is also presented. The branching patterns of three miniature trees that were CT scanned are used to showcase the statistical modeling framework, and the differences in their structural complexity are reflected in the results. Overall, the most important determinant of a tree structure appears to be the length of the branches attached to the trunk. This variable impacts the characteristics of all the other branches of the tree.

Phytomicrobiome Coordination Signals Hold Potential for Climate Change-Resilient Agriculture

A plant growing under natural conditions is always associated with a substantial, diverse, and well-orchestrated community of microbes-the phytomicrobiome. The phytomicrobiome genome is larger and more fluid than that of the plant. The microbes of the phytomicrobiome assist the plant in nutrient uptake, pathogen control, stress management, and overall growth and development. At least some of this is facilitated by the production of signal compounds, both plant-to-microbe and microbe back to the plant. This is best characterized in the legume nitrogen fixing and mycorrhizal symbioses. More recently lipo-chitooligosaccharide (LCO) and thuricin 17, two microbe-to-plant signals, have been shown to regulate stress responses in a wide range of plant species. While thuricin 17 production is constitutive, LCO signals are only produced in response to a signal from the plant. We discuss how some signal compounds will only be discovered when root-associated microbes are exposed to appropriate plant-to-microbe signals (positive regulation), and this might only happen under specific conditions, such as abiotic stress, while others may only be produced in the absence of a particular plant-to-microbe signal molecule (negative regulation). Some phytomicrobiome members only elicit effects in a specific crop species (specialists), while other phytomicrobiome members elicit effects in a wide range of crop species (generalists). We propose that some specialists could exhibit generalist activity when exposed to signals from the correct plant species. The use of microbe-to-plant signals can enhance crop stress tolerance and could result in more climate change resilient agricultural systems.

Box-Counting Dimension Revisited: Presenting an Efficient Method of Minimizing Quantization Error and an Assessment of the Self-Similarity of Structural Root Systems

Fractal dimension (FD), estimated by box-counting, is a metric used to characterize plant anatomical complexity or space-filling characteristic for a variety of purposes. The vast majority of published studies fail to evaluate the assumption of statistical self-similarity, which underpins the validity of the procedure. The box-counting procedure is also subject to error arising from arbitrary grid placement, known as quantization error (QE), which is strictly positive and varies as a function of scale, making it problematic for the procedure's slope estimation step. Previous studies either ignore QE or employ inefficient brute-force grid translations to reduce it. The goals of this study were to characterize the effect of QE due to translation and rotation on FD estimates, to provide an efficient method of reducing QE, and to evaluate the assumption of statistical self-similarity of coarse root datasets typical of those used in recent trait studies. Coarse root systems of 36 shrubs were digitized in 3D and subjected to box-counts. A pattern search algorithm was used to minimize QE by optimizing grid placement and its efficiency was compared to the brute force method. The degree of statistical self-similarity was evaluated using linear regression residuals and local slope estimates. QE, due to both grid position and orientation, was a significant source of error in FD estimates, but pattern search provided an efficient means of minimizing it. Pattern search had higher initial computational cost but converged on lower error values more efficiently than the commonly employed brute force method. Our representations of coarse root system digitizations did not exhibit details over a sufficient range of scales to be considered statistically self-similar and informatively approximated as fractals, suggesting a lack of sufficient ramification of the coarse root systems for reiteration to be thought of as a dominant force in their development. FD estimates did not characterize the scaling of our digitizations well: the scaling exponent was a function of scale. Our findings serve as a caution against applying FD under the assumption of statistical self-similarity without rigorously evaluating it first.

Concepts and Analyses in the CT Scanning of Root Systems and Leaf Canopies: A Timely Summary

Non-medical applications of computed tomography (CT) scanning have flourished in recent years, including in Plant Science. This Perspective article on CT scanning of root systems and leaf canopies is intended to be of interest to three categories of readers: those who have not yet tried plant CT scanning, and should find inspiration for new research objectives; readers who are on the learning curve with applications-here is helpful advice for them; and researchers with greater experience-the field is evolving quickly and it is easy to miss aspects. Our conclusion is that CT scanning of roots and canopies is highly demanding in terms of technology, multidisciplinarity and big-data analysis, to name a few areas of expertise, but eventually, the reward for researchers is directly proportional!

Computed tomography scanning can monitor the effects of soil medium on root system development: an example of salt stress in corn

Seeds and young seedlings often encounter high soluble salt levels in the upmost soil layers, impeding vigorous growth by affecting root establishment. Computed tomography (CT) scanning used at low X-ray doses can help study root development in such conditions non-destructively, because plants are allowed to grow throughout the experiment. Using a high-resolution Toshiba XVision CT scanner, we studied corn (Zea mays L.) root growth under optimal and salt-stressed conditions in 3D and on a weekly basis over 3 weeks. Two groups of three corn plants were grown in the controlled environment of a growth chamber, in mid-sized plastic pots filled with sieved and autoclaved sand. Seedlings were subjected to first CT scanning 1 week after seed planting. Our main research objectives concerning root systems were: (i) to quantify structural complexity from fractal dimensions estimated on skeletal 3-D images built from CT scanning data; (ii) to measure growth from volumes and lengths and the derived relative rates and increments, after isolating primary and secondary roots from the soil medium in CT scanning data; and (iii) to assess differences in complexity and growth per week and over Weeks 1-3 for groups of corn plants. Differences between groups were present from Week 1; starting in Week 2 secondary roots were present and could be isolated, which refined the complexity and growth analyses of root systems. Besides expected Week main effects (P < 0.01 or 0.05), Week × Group interaction (P < 0.05 or 0.10), and Group main effects were observed. Graphical, quantitative, and statistical analyses of CT scanning data were thus completed at an unprecedented level, and provided new and important insights regarding root system development. Repeated CT scanning is the key to a better understanding of the establishment in the soil medium of crop plants such as corn and the assessment of salt stress effects on developing root systems, in complexity, volume, and length.

Crown traits of coniferous trees and their relation to shade tolerance can differ with leaf type: a biophysical demonstration using computed tomography scanning data

Plant light interception and shade tolerance are intrinsically related in that they involve structural, morphological and physiological adaptations to manage light capture for photosynthetic utilization, in order to sustain survival, development and reproduction. At the scale of small-size trees, crown traits related to structural geometry of branching pattern and space occupancy through phyllotaxis can be accurately evaluated in 3D, using computed tomography (CT) scanning data. We demonstrate this by scrutinizing the crowns of 15 potted miniature conifers of different species or varieties, classified in two groups based on leaf type (10 needlelike, 5 scalelike); we also test whether mean values of crown traits measured from CT scanning data and correlations with a shade tolerance index (STI) differ between groups. Seven crown traits, including fractal dimensions (FD1: smaller scales, FD2: larger scales) and leaf areas, were evaluated for all 15 miniature conifers; an average silhouette-to-total-area ratio was also calculated for each of the 10 needlelike-leaf conifers. Between-group differences in mean values are significant (P < 0.05) for STI, FD1, FD2, and the average leaf area displayed (ĀD). Between-group differences in sign and strength of correlations are observed. For example, the correlation between STI and FD1 is negative and significant (P < 0.10) for the needlelike-leaf group, but is positive and significant (P < 0.05) for the miniature conifers with scalelike leaves, which had lower STI and higher FD1 on average in our study; the positive correlation between STI and ĀD is significant (P < 0.05) for the scalelike-leaf group, and very moderate for the needlelike-leaf one. A contrasting physical attachment of the leaves to branches may explain part of the between-group differences. Our findings open new avenues for the understanding of fundamental plant growth processes; the information gained could be included in a multi-scale approach to tree crown modeling.

How do trees grow? Response from the graphical and quantitative analyses of computed tomography scanning data collected on stem sections

Tree growth, as measured via the width of annual rings, is used for environmental impact assessment and climate back-forecasting. This fascinating natural process has been studied at various scales in the stem (from cell and fiber within a growth ring, to ring and entire stem) in one, two, and three dimensions. A new approach is presented to study tree growth in 3D from stem sections, at a scale sufficiently small to allow the delineation of reliable limits for annual rings and large enough to capture directional variation in growth rates. The technology applied is computed tomography scanning, which provides - for one stem section - millions of data (indirect measures of wood density) that can be mapped, together with a companion measure of dispersion and growth ring limits in filigree. Graphical and quantitative analyses are reported for white spruce trees with circular vs non-circular growth. Implications for dendroclimatological research are discussed.

Virtual taphonomy using synchrotron tomographic microscopy reveals cryptic features and internal structure of modern and fossil plants

While more commonly applied in zoology, synchrotron radiation X-ray tomographic microscopy (SRXTM) is well-suited to nondestructive study of the morphology and anatomy of both fossil and modern plants. SRXTM uses hard X-rays and a monochromatic light source to provide high-resolution data with little beam-hardening, resulting in slice data with clear boundaries between materials. Anatomy is readily visualized, including various planes of section from a single specimen, as clear as in traditional histological sectioning at low magnifications. Thus, digital sectioning of rare or difficult material is possible. Differential X-ray attenuation allows visualization of different layers or chemistries to enable virtual 3-dimensional (3D) dissections of material. Virtual potential fossils can be visualized and digital tissue removal reveals cryptic underlying morphology. This is essential for fossil identification and for comparisons between assemblages where fossils are preserved by different means. SRXTM is a powerful approach for botanical studies using morphology and anatomy. The ability to gain search images in both 2D and 3D for potential fossils gives paleobotanists a tool--virtual taphonomy--to improve our understanding of plant evolution and paleobiogeography.

A double-digitising method for building 3D virtual trees with non-planar leaves: application to the morphology and light-capture properties of young beech trees (Fagus sylvatica)

We developed a double-digitising method combining a hand-held electromagnetic digitizer and a non-contact 3D laser scanner. The former was used to record the positions of all leaves in a tree and the orientation angles of their lamina. The latter served to obtain the morphology of the leaves sampled in the tree. As the scanner outputs a cloud of points, software was developed to reconstruct non-planar (NP) leaves composed of triangles, and to compute numerical shape parameters: midrib curvature, torsion and transversal curvature of the lamina. The combination of both methods allowed construction of 3D virtual trees with NP leaves. The method was applied to young beech trees (Fagus sylvatica L.) from different sunlight environments (from 1 to 100% incident light) in a forest in central France. Leaf morphology responded to light availability, with a more bent shape in well-lit leaves. Light interception at the leaf scale by NP leaves decreased from 4 to 10% for shaded and sunlit leaves compared with planar leaves. At the tree scale, light interception by trees made of NP leaves decreased by 1 to 3% for 100% to 1% light, respectively.

Assessment of common scab-inducing pathogen effects on potato underground organs via computed tomography scanning

Common scab caused by Streptomyces scabies is a major bacterial disease of potato (Solanum tuberosum). Its best known symptom is superficial lesions on the surface of progeny potato tubers, observed at harvesting. In this study, effects of S. scabies on space occupancy by underground organs and on structural complexity of root systems are investigated during growth via computed tomography (CT) scanning. Two groups of potato plants were grown in a greenhouse in middle-sized plastic pots. Using a high-resolution X-ray CT scanner formerly used for medical applications, their underground organs and surrounding medium (sieved and autoclaved homogeneous sand) were submitted to CT scanning 4, 6, and 8 weeks after planting. For one group, sand was inoculated with the common scab-inducing pathogen (S. scabies EF-35) at potting. Space occupancy by underground organs was estimated via curve fitting applied to histograms of CT scan data, while three-dimensional skeletal images were used for fractal analysis. Root systems of diseased plants were found to be less complex than those of healthy plants 4 weeks after planting, and the relative growth rates derived from space occupancy measures were of different sign between the two groups from week 4 to week 8.