Building a better foundation: improving root‐trait measurements to understand and model plant and ecosystem processes
Michael McCormackDali GuoColleen M. IversenWeile ChenDavid M. EissenstatChristopher W. FernandezLe LiChengen MaZeqing MaHendrik PoorterPeter B. ReichMarcin ZadwornyAmy E. Zanne
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Trait-based approaches provide a useful framework to investigate plant strategies for resource acquisition, growth, and competition, as well as plant impacts on ecosystem processes. Despite significant progress capturing trait variation within and among stems and leaves, identification of trait syndromes within fine-root systems and between fine roots and other plant organs is limited. Here we discuss three underappreciated areas where focused measurements of fine-root traits can make significant contributions to ecosystem science. These include assessment of spatiotemporal variation in fine-root traits, integration of mycorrhizal fungi into fine-root-trait frameworks, and the need for improved scaling of traits measured on individual roots to ecosystem-level processes. Progress in each of these areas is providing opportunities to revisit how below-ground processes are represented in terrestrial biosphere models. Targeted measurements of fine-root traits with clear linkages to ecosystem processes and plant responses to environmental change are strongly needed to reduce empirical and model uncertainties. Further identifying how and when suites of root and whole-plant traits are coordinated or decoupled will ultimately provide a powerful tool for modeling plant form and function at local and global scales.Keywords:
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This chapter contains sections titled: Measurement of root systems Root system development Size and distribution of root systems Root:shoot allocation of dry matter Root longevity and turnover Modelling of root systems
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Knowledge of the extent and distribution of tree root systems is essential for managing trees in the built environment. Despite recent advances in root detection tools, published research on tree root architecture in urban settings has been limited and only partially synthesized. Root growth patterns of urban trees may differ considerably from similar species in forested or agricultural environments. This paper reviews literature documenting tree root growth in urban settings as well as literature addressing root architecture in nonurban settings that may contribute to present understanding of tree roots in built environments. Although tree species may have the genetic potential for generating deep root systems (>2 m), rooting depth in urban situations is frequently restricted by impenetrable or inhospitable soil layers or by underground infrastructure. Lateral root extent is likewise subject to restriction by dense soils under hardscape or by absence of irrigation in dry areas. By combining results of numerous studies, the authors of this paper estimated the radius of an unrestricted root system initially increases at a rate of approximately 38 to 1, compared to trunk diameter; however, this ratio likely considerably declines as trees mature. Roots are often irregularly distributed around the tree and may be influenced by cardinal direction, terrain, tree lean, or obstacles in the built environment. Buttress roots, tap roots, and other root types are also discussed.
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The essential properties of the roots of complex semisimple Lie algebras may be captured in the idea of an abstract 'root system'. In this chapter, we shall develop the basic theory of root systems. Our eventual aim, achieved in Chapters 13 and 14, will be to use root systems to classify the complex semisimple Lie algebras. Root systems have since been discovered to be important in many other areas of mathematics, so while this is probably your first encounter with root systems, it may well not be your last! In MathSciNet, the main database for research papers in mathematics, there are, at the time of writing, 297 papers whose title contains the words 'root system', and many thousands more in which root systems are mentioned in the text.
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Abstract Functional-structural root system models combine functional and structural root traits to represent the growth and development of root systems. In general, they are characterized by a large number of growth, architectural and functional root parameters, generating contrasted root systems evolving in a highly nonlinear environment (soil, atmosphere), which makes unclear what impact of each single root system on root system functioning actually is. On the other end of the root system modelling continuum, macroscopic root system models associate to each root system instance a set of plant-scale, easily interpretable parameters. However, as of today, it is unclear how these macroscopic parameters relate to root-scale traits and whether the upscaling of local root traits are compatible with macroscopic parameter measurements. The aim of this study was to bridge the gap between these two modelling approaches by providing a fast and reliable tool, which eventually can help performing plant virtual breeding. We describe here the MAize Root System Hydraulic Architecture soLver (MARSHAL), a new efficient and user-friendly computational tool that couples a root architecture model (CRootBox) with fast and accurate algorithms of water flow through hydraulic architectures and plant-scale parameter calculations, and a review of architectural and hydraulic parameters of maize. To illustrate the tool’s potential, we generated contrasted maize hydraulic architectures that we compared with architectural (root length density) and hydraulic (root system conductance) observations. Observed variability of these traits was well captured by model ensemble runs We also analyzed the multivariate sensitivity of mature root system conductance, mean depth of uptake, root system volume and convex hull to the input parameters to highlight the key parameters to vary for efficient virtual root system breeding. MARSHAL enables inverse optimisations, sensitivity analyses and virtual breeding of maize hydraulic root architecture. It is available as an R package, an RMarkdown pipeline, and a web application. One-sentence summary We developed a dynamic hydraulic-architectural model of the root system, parameterized for maize, to generate contrasted hydraulic architectures, compatible with field and lab observations and that can be further analyzed in soil-root system models for virtual breeding. Authors contributions F.M., X.D., M.J. and G.L. designed the study and defined its scope; F.M. and G.L. developed the model while associated tools were created by A.H. and G.L.; F.M. ran the model simulations and analyzed the results together with M.J and G.L.; F.M. and M.J. wrote the first version of this manuscript; all co-authors critically revised it.
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Resource acquisition, one of the major functions of roots, can contribute to crop growth and mitigating environmental impacts. The spatio-temporal distribution of roots in the soil in relation to the dynamics of the soil resources is critical in resource acquisition. Root distribution is determined by root system development. The root system consists of many individual roots of different types and ages. Each individual root has specific development, resource acquisition, and transport traits, which change with root growth. The integration of individual root traits in the root system could exhibit crop performance in the various environments via root distribution in the soil. However, the relationship between individual root traits and the pattern of root distribution is complicated. To understand this complicated relationship, we need to evaluate enormous numbers of individual root traits and understand the relationship between individual root development and root distribution as well as the integrated functions of individual root traits along with dynamics of resources in the soil.
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Abstract Well‐adapted root systems allow plants to grow under resource‐limiting environmental conditions and are important determinants of yield in agricultural systems. Important staple crops such as rice and maize belong to the family of grasses, which develop a complex root system that consists of an embryonic root system that emerges from the seed, and a postembryonic nodal root system that emerges from basal regions of the shoot after germination. While early seedling establishment is dependent on the embryonic root system, the nodal root system, and its associated branches, gains in importance as the plant matures and will ultimately constitute the bulk of below‐ground growth. In this review, we aim to give an overview of the different root types that develop in cereal grass root systems, explore the different physiological roles they play by defining their anatomical features, and outline the genetic networks that control their development. Through this deconstructed view of grass root system function, we provide a parts‐list of elements that function together in an integrated root system to promote survival and crop productivity.
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<p>The combination of functional-structural root-system models with root architectures derived from non-invasive imaging is a promising approach for gaining a better understanding of root-soil interaction processes. However, root architectures can often not be fully recovered using imaging, which subsequently affects the assessment of function via the functional-structural root models. In this study, we explored theoretical and actual possibilities of root system reconstruction from MRI and X-ray CT images. Experiments with water-filled capillaries showed the same minimum detectable diameter for both MRI and X-ray CT for the used parameter setup. Experiments with soil-grown lupine roots, however, showed significantly lower root system recovery fractions for MRI than for X-ray CT, from which most roots thicker than 0.2&#160;mm could be recovered. MRI allowed root signal detection below voxel resolution; however, the connection of this signal to a continuous root structure proved difficult for large, crowded root systems. Furthermore, soil moisture levels >30% hampered root system recovery from MRI scans in experiments with pure sand. To overcome the problem of low root system recovery fractions, we developed a new method that uses incomplete root systems as a scaffold onto which missing roots are simulated using information from WinRhizo measurements. Comparisons of root length within subsamples of semi-virtual root systems and root systems derived from X-ray CT scans showed good agreement. Evaluation of hydraulic root architecture measures of incomplete root system scaffolds and semi-virtual root systems proved the importance of using complete root system reconstructions to simulate root water uptake. Semi-virtual root reconstruction thus appears to be a promising technique to complete root systems for subsequent use in functional-structural root models.</p>
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Root-root interactions significantly impact the formation of architectural root phenotypes, yet are poorly understood. Phenotype formation is impacted by sensing of soil resources and exudates of neighboring plants (Nord et al., 2011; Wang et al., 2021), which motivates the need to accurately quantify this phenomenon into its underlying causes. Currently, we are developing a complete experimental system for root-root interactions. A mesh frame has been designed to support the growth of two mature plant root systems. The frame is inserted into a large mesocosm, filled with a sand/soil mixture, and two plants are grown. To harvest, the mesocosm is disassembled and the sand/soil is gently washed away. Root systems are left suspended in the mesh and using a Canon EOS Rebel T5, ~500 total photos are taken at 10 different angles ranging from below to above the roots, 360° around the frame. DIRT/3D is used to construct 3D models and extract data from individual root systems. We are in the process of improving our data extraction methods to include spatial traits relative to the two root systems. To do so, we dye the root systems right before harvesting. The difference in coloration allows the use of a deep learning model based on U-net architecture to perform image segmentation and separate roots in the 3D models. Our next step is to run a larger experiment with 10 mesh frames. This will provide statistical significance for trait identification and adaptation of DIRT/3D for root-root interaction data extraction and analysis.
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