Articles | Volume 8, issue 1
SOIL, 8, 223–235, 2022
https://doi.org/10.5194/soil-8-223-2022
SOIL, 8, 223–235, 2022
https://doi.org/10.5194/soil-8-223-2022
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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on soil-2021-79', Anonymous Referee #1, 19 Oct 2021
    • AC1: 'Reply on RC1', Raphael Viscarra Rossel, 02 Dec 2021
  • RC2: 'Comment on soil-2021-79', Lauric Cécillon, 24 Nov 2021
    • AC2: 'Reply on RC2', Raphael Viscarra Rossel, 02 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (03 Dec 2021) by Nicolas P.A. Saby
AR by Raphael Viscarra Rossel on behalf of the Authors (29 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (21 Jan 2022) by Nicolas P.A. Saby
AR by Raphael Viscarra Rossel on behalf of the Authors (07 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (09 Feb 2022) by Nicolas P.A. Saby
ED: Publish as is (19 Feb 2022) by Kristof Van Oost(Executive Editor)
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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.