Nodulation number tempers the relative importance of stochastic processes in the assembly of soybean root-associated communities

被引:2
|
作者
Wang, Lei [1 ,2 ]
Jiao, Yan [1 ]
Bi, Yingdong [3 ]
Hu, Yanli [1 ]
Jiang, Yan [1 ]
Wang, Shaodong [1 ]
Wang, Sui [1 ]
机构
[1] Northeast Agr Univ, Key Lab Soybean Biol, Chinese Minist Educ, Harbin 150030, Peoples R China
[2] Northeast Agr Univ, Sch Resources & Environm, Harbin 150030, Peoples R China
[3] Heilongjiang Acad Agr Sci, Inst Crop Cultivat & Tillage, Harbin 150028, Peoples R China
来源
ISME COMMUNICATIONS | 2023年 / 3卷 / 01期
关键词
BULK SOIL; RHIZOSPHERE; MICROBIOTA; DIVERSITY; ECOLOGY; NICHE;
D O I
10.1038/s43705-023-00296-8
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Identifying the ecological forces that structure root-associated microbial communities is an essential step toward more sustainable agriculture. Legumes are widely utilized as model plants to study selective forces and their functioning in plant-microbial interactions owing to their ability to establish mutualism with rhizobia. Root nodules act as symbiotic organs to optimize the cost-benefit balance in this mutualistic relationship by modulating the number of nodules. However, it is not known whether the number of nodules is related to the structure of root-associated bacterial communities. Here, the root-associated bacterial communities of soybean grown in native soil by means of soybean cultivars with super- or normal nodulation were investigated across four developmental stages. We compared ecological processes between communities and found decreased relative importance of neutral processes for super-nodulating soybean, although the overall structures resembled those of normal-nodulating soybean. We identified the generalist core bacterial populations in each root-associated compartment, that are shared across root-associated niches, and persist through developmental stages. Within core bacterial species, the relative abundances of bacterial species in the rhizosphere microbiome were linked to host-plant functional traits and can be used to predict these traits from microbes using machine learning algorithms. These findings broaden the comprehensive understanding of the ecological forces and associations of microbiotas in various root-associated compartments and provide novel insights to integrate beneficial plant microbiomes into agricultural production to enhance plant performance.
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页数:10
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