- 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 copperManagement 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.…
MetadatenAuthor details: | Bertrand FournierORCiDGND, Magdalena SteinerORCiD, Xavier Brochet, Florine DegruneORCiD, Jibril MammeriORCiD, Diogo Leite CarvalhoORCiD, Sara Leal SiliceoORCiD, Sven BacherORCiDGND, Carlos Andrés Peña-ReyesORCiD, Thierry Jean HegerORCiDGND |
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DOI: | https://doi.org/10.1016/j.ecolind.2022.108955 |
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ISSN: | 1470-160X |
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ISSN: | 1872-7034 |
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Title of parent work (English): | Ecological indicators : integrating monitoring, assessment and management |
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Subtitle (English): | a case study in vineyard ecosystems |
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Publisher: | Elsevier |
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Place of publishing: | Amsterdam |
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Publication type: | Article |
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Language: | English |
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Date of first publication: | 2022/05/13 |
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Publication year: | 2022 |
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Release date: | 2024/01/31 |
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Tag: | Biomonitoring; Machine learning; Microbial ecology; Predictive model; Soil; Soil function; quality |
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Volume: | 139 |
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Article number: | 108955 |
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Number of pages: | 8 |
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Funding institution: | HES-SO [78046]; CRP; Swiss Federal Office for the Environment; [19.0061.PJ.PZ, D-91173401/988, 00.5005.PZ/3A97E39C8,; 00.5005.PZ/A58E8CC1A]; DFG [FO 1420/1-1]; WISNA program from the German; Federal Ministry of Education and Research; PromESSinG project; Swiss; National Science Foundation [40FA40_158390] |
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Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Umweltwissenschaften und Geographie |
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DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
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| 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
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Peer review: | Referiert |
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Publishing method: | Open Access / Gold Open-Access |
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| DOAJ gelistet |
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License (German): | CC-BY - Namensnennung 4.0 International |
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