BertrandFournierab, Magdalena Steinerc, Xavier Brochetde, Florine Degruneab, Jibril Mammerid, Diogo Leite Carvalhode1, Sara Leal Siliceod, SvenBacherc, Carlos AndrésPeña-Reyesde,Thierry J.Hegerb
a Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany
b Soil Science and Environment Group, CHANGINS, HES-SO University of Applied Sciences and Arts Western Switzerland, Route de Duillier 50, 1260 Nyon, Switzerland
c Applied Ecology Group, Department of Biology, Ch. du Musée 10, CH-1700 Fribourg, SwitzerlanddSchool of Business and Engineering Vaud (HEIG-VD), HES-SO University of Applied Sciences and Arts Western Switzerland, Switzerland
e Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
Received 13 March 2022, Revised 4 May 2022, Accepted 7 May 2022, Available online 13 May 2022, Version of Record 13 May 2022.
Ecological Indicators, Volume 139, 2022, 108955, ISSN 1470-160X
Management of agricultural soil quality requires fast and cost-efficient methods to identify multiple stressors that can affect soil organisms and associated ecological processes. Here, we propose to use soil protists which have a great yet poorly explored potential for bioindication. They are ubiquitous, highly diverse, and respond to various stresses to agricultural soils caused by frequent management or environmental changes. We test an approach that combines metabarcoding data and machine learning algorithms to identify potential stressors of soil protist community composition and diversity. We measured 17 key variables that reflect various potential stresses on soil protists across 132 plots in 28 Swiss vineyards over 2 years. We identified the taxa showing strong responses to the selected soil variables (potential bioindicator taxa) and tested for their predictive power. Changes in protist taxa occurrence and, to a lesser extent, diversity metrics exhibited great predictive power for the considered soil variables. Soil copper concentration, moisture, pH, and basal respiration were the best predicted soil variables, suggesting that protists are particularly responsive to stresses caused by these variables. The most responsive taxa were found within the clades Rhizaria and Alveolata. Our results also reveal that a majority of the potential bioindicators identified in this study can be used across years, in different regions and across different grape varieties. Altogether, soil protist metabarcoding data combined with machine learning can help identifying specific abiotic stresses on microbial communities caused by agricultural management. Such an approach provides complementary information to existing soil monitoring tools that can help manage the impact of agricultural practices on soil biodiversity and quality.
Keywords: Biomonitoring; Machine learning; Predictive model; Soil function; Soil quality; Microbial ecology