Statistical analysis and modeling of root measures for the description of spatiotemporal root patterns, using experimental and simulated image data gained by X-ray CT and root architecture models

使用 X 射线 CT 和根结构模型获得的实验和模拟图像数据，对根测量进行统计分析和建模，以描述时空根模式

• 批准号: 426456278
• 负责人: Professor Dr. Volker Schmidt
• 金额: --
• 依托单位: Institut für Stochastik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/426456278?language=en
• 关键词: Statistical; analysis; modeling; root; measures

The 3D microstructure of roots plays a key role for biological, chemical and physical processes that drive rhizosphere and root structure formation and function. X-ray computed tomography (CT) is a powerful technology to study spatiotemporal root growth patterns in 3D. However, simulated root architectures provide additional insights, e.g. through faster data acquisition and higher temporal resolution. In both cases, i.e. in experimental and virtual investigations of root growth patterns, large amounts of complex image data are generated, which need to be statistically analyzed and modeled using as few as possible model parameters. In a recent publication together with the group of D. Vetterlein (UFZ), we proposed a root distance model, which is able to describe root growth patterns throughout all stages in the first weeks of growth of Vicia faba. In a further paper with the group of A. Schnepf (FZJ), we investigated the connection between the input parameters of the 3D root architecture model CRootBox and various measures of the simulated root systems, like root length density and volume of the convex hull.The aim of the present project is to continue and extend the fruitful collaborations with the Vetterlein and Schnepf groups. First, we continue to statistically analyze (experimentally observed and simulated) root growth patterns from the soil perspective. In addition to analyzing entire root systems via root distance models, we develop local root distance models with respect to specific classes of root segments, e.g. segments which are older (proximal to the seed) or segments being at, or near, the tips of roots. This will give us more detailed insight into the dynamics of root growth and function. Furthermore, quantitative relationships will be established between the input parameters of the 3D root architecture model CRootBox and various root measures. Multivariate approaches such as copulas provide the mathematical tools to build parametric meta-models for vectors of (correlated) root measures. The results will be used to develop a universally applicable approach for the target-oriented calibration of root architecture models. In particular, we will show how methods of machine learning can be combined with the results obtained, in order to calibrate CRootBox by means of tomographic root image data or derived measures. An additional topic is the statistical description of geometrical root patterns to distinguish between purely random, even and clustering morphologies. Methods of stochastic geometry provide yet another perspective in the analysis of growing root systems and will be used to study, e.g. the correlation of root piercing point patterns in planar (e.g. vertical or horizontal) sections of soil with chemical 2D maps. Last but not least, we will perform a comparative statistical analysis of root measures in constrained and unconstrained root architectures.

根的3D微结构在驱动根际和根结构形成和功能的生物，化学和物理过程中起关键作用。 X射线计算机断层扫描（CT）是一项强大的技术，用于研究3D中的时空根生长模式。但是，模拟的根架构提供了其他见解，例如通过更快的数据获取和更高的时间分辨率。在这两种情况下，即在对根生长模式的实验和虚拟研究中，都会生成大量的复杂图像数据，这些图像数据需要经过统计分析和建模，并使用尽可能少的模型参数进行建模。 在最近的出版物与D. vetterlein（UFZ）的一组中，我们提出了一个根距离模型，该模型能够在Vicia Faba的最初几周内描述所有阶段的根生长模式。在与A. Schnepf（FZJ）组的另一篇论文中，我们研究了3D根体系结构模型CROOTBOX的输入参数与模拟根系的各种度量之间的连接，例如凸出的根长度和凸的量。首先，我们从土壤的角度继续统计地分析（实验观察和模拟）根生长模式。除了通过根距离模型分析整个根系外，我们还针对特定类别的根部片段开发了局部根距离模型，例如较大的段（与种子近端）或片段位于根的尖端或附近。这将使我们对根生长和功能的动态更详细地了解。此外，将在3D根体系结构模型CROOTBOX的输入参数和各种根措施之间建立定量关系。多元方法（例如Copulas）提供了数学工具，以构建（相关）根测量向量的参数元模型。结果将用于开发一种普遍适用的方法，用于针对根体系结构模型的目标校准。特别是，我们将展示如何将机器学习方法与获得的结果相结合，以通过层析成像根图像数据或派生的度量来校准CROOTBOX。另一个主题是几何词根模式的统计描述，以区分纯粹随机，甚至聚集形态。随机几何形状的方法在分析生长的根系中提供了另一个观点，将用于研究，例如与化学2D地图的土壤平面（例如垂直或水平）部分中的根穿孔点模式的相关性。最后但并非最不重要的一点是，我们将对受约束和无约束的根架构中的根度量进行比较统计分析。