Dataset Information

Visualising spatio-temporal distributions of assimilated carbon translocation and release in root systems of leguminous plants.

ABSTRACT: The release of rhizodeposits differs depending on the root position and is closely related to the assimilated carbon (C) supply. Therefore, quantifying the C partitioning over a short period may provide crucial information for clarifying root-soil carbon metabolism. A non-invasive method for visualising the translocation of recently assimilated C into the root system inside the rhizobox was established using 11CO2 labelling and the positron-emitting tracer imaging system. The spatial distribution of recent 11C-photoassimilates translocated and released in the root system and soil were visualised for white lupin (Lupinus albus) and soybean (Glycine max). The inputs of the recently assimilated C in the entire root that were released into the soil were approximately 0.3%-2.9% for white lupin within 90?min and 0.9%-2.3% for soybean within 65?min, with no significant differences between the two plant species; however, the recently assimilated C of lupin was released at high concentrations in specific areas (hotspots), whereas that of soybean was released uniformly in the soil. Our method enabled the quantification of the spatial C allocations in roots and soil, which may help to elucidate the relationship between C metabolism and nutrient cycling at specific locations of the root-soil system in response to environmental conditions over relatively short periods.

SUBMITTER: Yin YG

PROVIDER: S-EPMC7289824 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

Visualising spatio-temporal distributions of assimilated carbon translocation and release in root systems of leguminous plants.

Yin Yong-Gen YG Suzui Nobuo N Kurita Keisuke K Miyoshi Yuta Y Unno Yusuke Y Fujimaki Shu S Nakamura Takuji T Shinano Takuro T Kawachi Naoki N

Scientific reports 20200611 1

The release of rhizodeposits differs depending on the root position and is closely related to the assimilated carbon (C) supply. Therefore, quantifying the C partitioning over a short period may provide crucial information for clarifying root-soil carbon metabolism. A non-invasive method for visualising the translocation of recently assimilated C into the root system inside the rhizobox was established using <sup>11</sup>CO<sub>2</sub> labelling and the positron-emitting tracer imaging system. T ...[more]

PMID: 32528026

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Project description:Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level.

Project description:Background The root-associated microbiome has been of keen research interest especially in the last decade due to the large potential for increasing overall plant performance in agricultural systems. Studies about spatio-temporal variation of the root-associated microbiome focused so far primarily on community-compositional changes of annual plants, while little is known about their perennial counterparts. The aim of this work was to get deep insight into the spatial patterns and temporal dynamics of the root associated microbiota of apple trees. Results The bacterial community structure in rhizospheric soil and endospheric root material from orchard-grown apple trees was characterized based on 16S rRNA gene amplicon sequencing. At the small scale, the rhizosphere and endosphere bacterial communities shifted gradually with increasing root size diameter (PERMANOVA R2-values up to 0.359). At the larger scale, bulk soil heterogeneity introduced variation between tree individuals, especially in the rhizosphere microbiota, while the presence of a root pathogen was contributing to tree-to-tree variation in the endosphere microbiota. Moreover, the communities of both compartments underwent seasonal changes and displayed year-to-year variation (PERMANOVA R2-values of 0.454 and 0.371, respectively). Conclusions The apple tree root-associated microbiota can be spatially heterogeneous at field scale due to soil heterogeneities, which particularly influence the microbiota in the rhizosphere soil, resulting in tree-to-tree variation. The presence of pathogens can contribute to this variation, though primarily in the endosphere microbiota. Smaller-scale spatial heterogeneity is observed in the rhizosphere and endosphere microbiota related to root diameter, likely influenced by root traits and processes such as rhizodeposition. The microbiota is also subject to temporal variation, including seasonal effects and annual variation. As a consequence, responses of the tree root microbiota to further environmental cues should be considered in the context of this spatio-temporal variation. Supplementary Information The online version contains supplementary material available at 10.1186/s40793-022-00427-z.

Dataset Information

Visualising spatio-temporal distributions of assimilated carbon translocation and release in root systems of leguminous plants.

Publications

Visualising spatio-temporal distributions of assimilated carbon translocation and release in root systems of leguminous plants.

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