TY  - JOUR
A1  - Fournier, Bertrand
A1  - Steiner, Magdalena
A1  - Brochet, Xavier
A1  - Degrune, Florine
A1  - Mammeri, Jibril
A1  - Carvalho, Diogo Leite
A1  - Siliceo, Sara Leal
A1  - Bacher, Sven
A1  - Peña-Reyes, Carlos Andrés
A1  - Heger, Thierry Jean
T1  - Toward the use of protists as bioindicators of multiple stresses in agricultural soils
BT  - a case study in vineyard ecosystems
JF  - Ecological indicators : integrating monitoring, assessment and management
N2  - 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.
KW  - Biomonitoring
KW  - Machine learning
KW  - Predictive model
KW  - Soil function
KW  - Soil
KW  - quality
KW  - Microbial ecology
Y1  - 2022
U6  - https://doi.org/10.1016/j.ecolind.2022.108955
SN  - 1470-160X
SN  - 1872-7034
VL  - 139
PB  - Elsevier
CY  - Amsterdam
ER  -