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    Soil bacterial abundance and diversity better explained and predicted with spectro-transfer functions

    Access Status
    Fulltext not available
    Authors
    Yang, Y.
    Viscarra Rossel, Raphael
    Li, S.
    Bissett, A.
    Lee, J.
    Shi, Z.
    Behrens, T.
    Court, L.
    Date
    2019
    Type
    Journal Article
    
    Metadata
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    Citation
    Yang, Y. and Viscarra Rossel, R. and Li, S. and Bissett, A. and Lee, J. and Shi, Z. and Behrens, T. et al. 2019. Soil bacterial abundance and diversity better explained and predicted with spectro-transfer functions. Soil Biology and Biochemistry. 129: pp. 29-38.
    Source Title
    Soil Biology and Biochemistry
    DOI
    10.1016/j.soilbio.2018.11.005
    ISSN
    0038-0717
    School
    School of Molecular and Life Sciences (MLS)
    URI
    http://hdl.handle.net/20.500.11937/73805
    Collection
    • Curtin Research Publications
    Abstract

    Soil bacteria play a critical role in the functioning of ecosystems but are challenging to investigate. We developed state-factor models with machine learning to understand better and to predict the abundance of 10 dominant phyla and bacterial diversities in Australian soils, the latter expressed by the Chao and Shannon indices. In the models, we used proxies for the edaphic, climatic, biotic and topographic factors, which included soil properties, environmental variables, and the absorbance at visible–near infrared (vis–NIR) wavelengths. From a cross-validation with all observations (n = 681), we found that our models explained 43–73% of the variance in bacterial phyla abundance and diversity. The vis–NIR spectra, which represent the organic and mineral composition of soil, were prominent drivers of abundance and diversity in the models, as were changes in the soil-water balance, potential evapotranspiration, and soil nutrients. From independent validations, we found that spectro-transfer functions could predict well the phyla Acidobacteria and Actinobacteria (R2 > 0.7) as well as other dominant phyla and the Chao and Shannon diversities (R2 > 0.5). Predictions of the phyla Firmicutes were the poorest (R2 = 0.42). The vis–NIR spectra markedly improved the explanatory power and predictability of the models.

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