Articles | Volume 8, issue 1
SOIL, 8, 223–235, 2022
SOIL, 8, 223–235, 2022
Original research article
25 Mar 2022
Original research article | 25 Mar 2022

Estimating soil fungal abundance and diversity at a macroecological scale with deep learning spectrotransfer functions

Yuanyuan Yang et al.

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Cited articles

Bachar, A., Al-Ashhab, A., Soares, M. I., Sklarz, M. Y., Angel, R., Ungar, E. D., and Gillor, O.: Soil microbial abundance and diversity along a low precipitation gradient, Microbial. Ecol., 60, 453–461, 2010. a
Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., De Wit, P., Sánchez-García, M., Ebersberger, I., de Sousa, F., Amend, A., Jumpponen, A., Unterseher, M., Kristiansson, E., Abarenkov, K., Bertrand, Y. J. K., Sanli, K., Eriksson, K. M., Vik, U., Veldre, V., and Nilsson, R. H.: Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data, Meth. Ecol. Evol., 4, 914–919,, 2013. a
BioPlatforms Australia: Biomes of Australian Soil Environments (BASE), BioPlatforms Australia [data set],, last access: 22 March 2022. a
Bissett, A. and Viscarra Rossel, R.: Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database (1.0), Zenodo [data set],, 2022. a
Bissett, A., Fitzgerald, A., Meintjes, T., Mele, P. M., Reith, F., Dennis, P. G., Breed, M. F., Brown, B., Brown, M. V., Brugger, J., Byrne, M., Caddy-Retalic, S., Carmody, B., Coates, D. J., Correa, C., Ferrari, B. C., Gupta, V. V. S. R., Hamonts, K., Haslem, A., Hugenholtz, P., Karan, M., Koval, J., Lowe, A. J., Macdonald, S., McGrath, L., Martin, D., Matt, M., North, K. I., Paungfoo-Lonhienne, C., Pendall, E., Phillips, L., Pirzl, R., Powell, J. R., Ragan, M. A., Schmidt, S., Seymour, N., Snape, I., Stephen, J. R., Stevens, M., Tinning, M., Williams, K., Yeoh, Y. K., Zammit, C. M., and Young, A.: Introducing BASE: the Biomes of Australian Soil Environments soil microbial diversity database, GigaScience, 5, s13742-016-0126-5,, 2016. a, b, c, d
Short summary
We present a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. It uses state-of-the-art machine learning with publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared wavelengths. The estimates could serve to supplement the more expensive molecular approaches towards a better understanding of soil fungal abundance and diversity in agronomy and ecology.