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Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
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    Area of Science:

    • Ecology
    • Microbiology
    • Biogeochemistry

    Background:

    • Microorganisms are key drivers of biogeochemical cycles.
    • Connecting microbial community dynamics to ecosystem functions under field conditions is complex.
    • Understanding these links is crucial for predicting nutrient cycling and ecosystem health.

    Purpose of the Study:

    • To develop and test a model-based approach for estimating the independent contributions of microbial community shifts to ecosystem properties.
    • To empirically validate the model using denitrification potential in arable land.
    • To assess the impact of microbial community structure on predicting ecosystem functioning.

    Main Methods:

    • A spatial survey of arable land with varying edaphic conditions and agricultural systems was conducted.
    • Denitrification potential was used as the model process to link microbial communities to ecosystem function.
    • Statistical modeling, including Akaike's information criterion (AICc) and R-squared values, was employed to assess predictor importance.

    Main Results:

    • Soil nitrate was a significant predictor of denitrification potential.
    • Incorporating microbial community variables (evenness, size, and specific genotype abundance) substantially improved model precision.
    • Biotic variables explained 28% of the variation, abiotic variables 50%, and combined variables 76% of the variation in denitrification potential.

    Conclusions:

    • The developed model effectively links microbial community structure to ecosystem functioning.
    • Including microbial community data in biogeochemical models enhances predictions of nutrient cycling.
    • This approach offers a valuable tool for understanding ecosystem functionality and microbial roles therein.