Abstract
Soil fungi play important roles in the functioning of ecosystems, but they are challenging to measure. Using a continental scale dataset, we developed and evaluated a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. The method relies on the development of spectro-transfer functions with state-of-the-art machine learning and using publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible--near infrared (vis–NIR) wavelengths, to estimate the relative abundances of the Ascomycota, Basidiomycota, Glomeromycota, Mortierellomycota and Mucoromycota and community diversity measured with the abundance-based coverage estimator (ACE) index. The machine learning algorithms tested were partial least squares regression (PLSR), random forest (RF), Cubist, support vector machines (SVM), Gaussian process regression (GPR), XG-boost (XGB) and one-dimensional convolutional neural networks (1D-CNNs). The spectro-transfer functions were validated with a 10-fold cross-validation (n = 577). The 1D-CNNs outperformed the other algorithms and could explain between 45 and 73 % of fungal relative abundance and diversity. The models were interpretable, and showed that soil nutrients, pH, bulk density, an ecosystem water balance (a proxy for aridity) and net primary productivity were important predictors, as were specific vis–NIR wavelengths that correspond to organic functional groups, iron oxide and clay minerals. Estimates of the relative abundance for Mortierellomycota and Mucoromycota produced R2 ≥ 0.60, while estimates of the abundance of the Ascomycota and Basidiomycota produced R2 values of 0.5 and 0.58, respectively. The spectro-transfer functions for the Glomeromycota and diversity were the poorest with R2 values of 0.48 and 0.45, respectively. There is no doubt that the method provides estimates that are less accurate than more direct measurements with conventional molecular approaches. However, once the spectro-transfer functions are developed, they can be used with very little cost, and could serve to supplement the more expensive and laborious molecular approaches for a better understanding of soil fungal abundance and diversity under different agronomic and ecological settings.
Highlights
Soil fungi are important components of microbial communities, which inhabit dynamic soil environments
The method relies on the development of spectro-transfer functions with state-ofthe-art machine learning and using publicly available data on soil and environmental proxies for edaphic, climatic, biotic and 5 topographic factors, and visible–near infrared wavelengths, to estimate the relative abundances of the Ascomycota, Basidiomycota, Glomeromycota, Mortierellomycota and Mucoromycota and community diversity measured with the abundance-based coverage estimator (ACE) index
We evaluated the estimates using the coefficient of determination (R2), the root mean squared error (RMSE), which measures inaccuracy, the standard deviation of the error (SDE), which measures imprecision and the mean error (ME), which measures 135 bias (Viscarra Rossel and McBratney, 1998)
Summary
Soil fungi are important components of microbial communities, which inhabit dynamic soil environments. They play critical functional roles as decomposers, mutualists, and pathogens (Li et al, 2019). Given the important functions that soil fungi perform, it is important to better characterise and understand their communities over large scales. Data on soil fungi are few or largely unavailable because the measurement of soil fungi, which needs field sampling, followed by culture-based analysis or DNA sequencing, are laborious, time-consuming and costly. Using soil sensing technologies, such as spectroscopy together with molecular approaches could greatly improve the utility of fungal 30 inventory data (Hart et al, 2020)
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