Scaling up taxon-specific microbial traits to predict community-level microbial activity in agricultural systems

被引:1
|
作者
Walkup, Jeth G. [1 ,2 ]
Morrissey, Ember M. [1 ,3 ]
机构
[1] West Virginia Univ, Div Plant & Soil Sci, Morgantown, WV 26506 USA
[2] Univ Calif Irvine, Dept Ecol & Evolutionary Biol, Irvine, CA 92697 USA
[3] West Virginia Univ, Dept Biol, Life Sci Bldg, Morgantown, WV 26501 USA
来源
关键词
Soil microbial ecology; Quantitative ecology; Microbial functional traits; GROSS NITROGEN MINERALIZATION; BIOMASS CARBON; SOIL; BACTERIAL; ECOSYSTEMS; MANAGEMENT; DIVERSITY; SIZE;
D O I
10.1016/j.soilbio.2024.109622
中图分类号
S15 [土壤学];
学科分类号
0903 ; 090301 ;
摘要
Soil microorganisms perform many important ecosystem functions including nitrogen (N) cycling which dictates plant productivity in agricultural ecosystems. Despite the importance of these communities, connecting microbial composition with ecosystem function has been a long standing challenge. Taxon-specific substrate assimilation traits, measured with quantitative stable isotope probing (qSIP), may provide a means to scale from microbial community composition to community-level process rates. To test the potential for scaling up taxonspecific N assimilation to predict community-level rates of carbon mineralization, N mineralization, and N immobilization we measured soil properties, microbial activity, and N assimilation using 15 N qSIP in soils from six distinct farm systems. N assimilation, measured as DNA 15 N enrichment, varied among taxa and within taxa across farms. Taxon specific N assimilation was aggregated to calculate a community-weighted mean, which when combined with measures of microbial biomass was used to estimate new microbial biomass N production. This estimate of new microbial biomass production reflects the growth of active microbes over the incubation period and related to microbial activity. The new microbial biomass N produced was predictive of soil C and N mineralization rates, explaining 37-47% of the observed variation across the six farming systems. This approach highlights the ability of trait-based methods to relate microbial community structure data to microbially mediated functional process rates. Such advances may enhance our ability to understand and manage microbially mediated processes, such as N cycling, in both natural and agricultural ecosystems.
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页数:10
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