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Connecting the dots between root cross-section images and modelling tools to create a high resolution root system hydraulic maps in Zea mays.

bioRxiv · 15 Dec 2020 · 10.1101/2020.12.15.422825

Abstract

Root hydraulic properties play a central role in the global water cycle, agricultural systems productivity, and ecosystem survival as they impact the global canopy water supply. However, the available experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging and their applicability on thin roots and small root segments is limited. There is a gap in methods enabling easy estimations of root hydraulic conductivities across a diversity of root types and at high resolution along root axes. In this case study, we analysed Zea mays (maize) plants of the var. B73 that were grown in pots for 14 days. Root cross-section data were used to extract anatomical measurements. We used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively), based on the generated anatomical networks and cell hydraulic properties from the literature. The root hydraulic conductivity maps obtained from the root cross-sections suggest significant functional variations along and between different root types. Predicted variations of kr along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem. The different anatomical features, as well as their evolution along the root type add significant variation to the kr estimation in between root type and along the root axe. Under the prism of root types, anatomy, and hydrophobic barriers, our results highlight the diversity of root radial and axial hydraulic conductivities, which may be veiled under low-resolution measurements of the root system hydraulic conductivity. While predictions of our root hydraulic maps match the range and trend of measurements reported in the literature, future studies could focus on the quantitative validation of hydraulic maps. From now on, a novel method, which turns root cross-section images into hydraulic maps will offer an inexpensive and easily applicable investigation tool for root hydraulics, in parallel to root pressure probing experiments. One-Sentence summaryThe use of cross-section images and modelling tools to generate a map the axial and radial hydraulic conductivity along different root types for the maize cultivar B73.

Plant phenotyping relevance

根の断面画像から解剖学的形質を抽出し、モデルで軸方向・半径方向の根 hydraulic conductivity を推定する手法が研究の中心であるため、植物表現型計測手法として収録する。

abstractWe used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively)
abstracta novel method, which turns root cross-section images into hydraulic maps will offer an inexpensive and easily applicable investigation tool for root hydraulics

Code and data availability

The paper provides two public, paper-specific assets: the GRANAR-MECHA coupling workflow (Jupyter/R repository with Zenodo DOI) and the B73_HydraulicMap repository containing the Rmarkdown script used to compute the root hydraulic maps plus all input and output data of the study.

Codepublic

ection can be visualized through different figures that show the 186 proportion of the water fluxes in each compartiment (apoplastic and symplastic fluxes). The whole script that was used to compute the root hydraulic maps from the root anatomical 188 measurement is presented as a Rmarkdown script stored in a GitHub repository (https://github.com/granar/B73_HydraulicMap doi: 10.5281/zenodo.4320861). In the same 190 repository are stored all input and output data of this study. 192 10 of 24 . CC-BY 4.0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint

Open resource ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861 · pdf-raw-page:10 lines:1-20

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